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Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Problem:We introduce the masked translation model (MTM) which combines encoding and decoding of sequences within the same model component. The MTM is based on the idea of masked language modeling and supports both autoregressive and non-autoregressive decoding strategies by simply changing the order of masking. In experiments on the WMT 2016 Romanian-English task, the MTM shows strong constant-time translation performance, beating all related approaches with comparable complexity. We also extensively compare various decoding strategies supported by the MTM, as well as several length modeling techniques and training settings.
Solution:
|
We use a transformer encoder to do translation by training it in the style of a masked translation model.
| 8
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Teacher:In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Teacher: Now, understand the problem? Solve this instance: Deep neural networks (DNNs) have been shown to over-fit a dataset when being trained with noisy labels for a long enough time. To overcome this problem, we present a simple and effective method self-ensemble label filtering (SELF) to progressively filter out the wrong labels during training. Our method improves the task performance by gradually allowing supervision only from the potentially non-noisy (clean) labels and stops learning on the filtered noisy labels. For the filtering, we form running averages of predictions over the entire training dataset using the network output at different training epochs. We show that these ensemble estimates yield more accurate identification of inconsistent predictions throughout training than the single estimates of the network at the most recent training epoch. While filtered samples are removed entirely from the supervised training loss, we dynamically leverage them via semi-supervised learning in the unsupervised loss. We demonstrate the positive effect of such an approach on various image classification tasks under both symmetric and asymmetric label noise and at different noise ratios. It substantially outperforms all previous works on noise-aware learning across different datasets and can be applied to a broad set of network architectures.
Student:
|
We propose a self-ensemble framework to train more robust deep learning models under noisy labeled datasets.
| 6
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Input: Detection of photo manipulation relies on subtle statistical traces, notoriously removed by aggressive lossy compression employed online. We demonstrate that end-to-end modeling of complex photo dissemination channels allows for codec optimization with explicit provenance objectives. We design a lightweight trainable lossy image codec, that delivers competitive rate-distortion performance, on par with best hand-engineered alternatives, but has lower computational footprint on modern GPU-enabled platforms. Our results show that significant improvements in manipulation detection accuracy are possible at fractional costs in bandwidth/storage. Our codec improved the accuracy from 37% to 86% even at very low bit-rates, well below the practicality of JPEG (QF 20).
Output:
|
We learn an efficient lossy image codec that can be optimized to facilitate reliable photo manipulation detection at fractional cost in payload/quality and even at low bitrates.
| 3
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Q: Semi-Supervised Learning (SSL) approaches have been an influential framework for the usage of unlabeled data when there is not a sufficient amount of labeled data available over the course of training. SSL methods based on Convolutional Neural Networks (CNNs) have recently provided successful results on standard benchmark tasks such as image classification. In this work, we consider the general setting of SSL problem where the labeled and unlabeled data come from the same underlying probability distribution. We propose a new approach that adopts an Optimal Transport (OT) technique serving as a metric of similarity between discrete empirical probability measures to provide pseudo-labels for the unlabeled data, which can then be used in conjunction with the initial labeled data to train the CNN model in an SSL manner. We have evaluated and compared our proposed method with state-of-the-art SSL algorithms on standard datasets to demonstrate the superiority and effectiveness of our SSL algorithm.
A:
|
We propose a new algorithm based on the optimal transport to train a CNN in an SSL fashion.
| 4
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Definition: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Input: Clustering is the central task in unsupervised learning and data mining. k-means is one of the most widely used clustering algorithms. Unfortunately, it is generally non-trivial to extend k-means to cluster data points beyond Gaussian distribution, particularly, the clusters with non-convex shapes (Beliakov & King, 2006). To this end, we, for the first time, introduce Extreme Value Theory (EVT) to improve the clustering ability of k-means. Particularly, the Euclidean space was transformed into a novel probability space denoted as extreme value space by EVT. We thus propose a novel algorithm called Extreme Value k-means (EV k-means), including GEV k-means and GPD k-means. In addition, we also introduce the tricks to accelerate Euclidean distance computation in improving the computational efficiency of classical k-means. Furthermore, our EV k-means is extended to an online version, i.e., online Extreme Value k-means, in utilizing the Mini Batch k-means to cluster streaming data. Extensive experiments are conducted to validate our EV k-means and online EV k-means on synthetic datasets and real datasets. Experimental results show that our algorithms significantly outperform competitors in most cases.
Output:
|
This paper introduces Extreme Value Theory into k-means to measure similarity and proposes a novel algorithm called Extreme Value k-means for clustering.
| 2
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Problem:We propose a new architecture for distributed image compression from a group of distributed data sources. The work is motivated by practical needs of data-driven codec design, low power consumption, robustness, and data privacy. The proposed architecture, which we refer to as Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC), is able to train distributed encoders and one joint decoder on correlated data sources. Its compression capability is much better than the method of training codecs separately. Meanwhile, for 10 distributed sources, our distributed system remarkably performs within 2 dB peak signal-to-noise ratio (PSNR) of that of a single codec trained with all data sources. We experiment distributed sources with different correlations and show how our methodology well matches the Slepian-Wolf Theorem in Distributed Source Coding (DSC). Our method is also shown to be robust to the lack of presence of encoded data from a number of distributed sources. Moreover, it is scalable in the sense that codes can be decoded simultaneously at more than one compression quality level. To the best of our knowledge, this is the first data-driven DSC framework for general distributed code design with deep learning.
Solution:
|
We introduce a data-driven Distributed Source Coding framework based on Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC).
| 8
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Given the task definition and input, reply with output. In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Crafting adversarial examples on discrete inputs like text sequences is fundamentally different from generating such examples for continuous inputs like images. This paper tries to answer the question: under a black-box setting, can we create adversarial examples automatically to effectively fool deep learning classifiers on texts by making imperceptible changes? Our answer is a firm yes. Previous efforts mostly replied on using gradient evidence, and they are less effective either due to finding the nearest neighbor word (wrt meaning) automatically is difficult or relying heavily on hand-crafted linguistic rules. We, instead, use Monte Carlo tree search (MCTS) for finding the most important few words to perturb and perform homoglyph attack by replacing one character in each selected word with a symbol of identical shape. Our novel algorithm, we call MCTSBug, is black-box and extremely effective at the same time. Our experimental results indicate that MCTSBug can fool deep learning classifiers at the success rates of 95% on seven large-scale benchmark datasets, by perturbing only a few characters. Surprisingly, MCTSBug, without relying on gradient information at all, is more effective than the gradient-based white-box baseline. Thanks to the nature of homoglyph attack, the generated adversarial perturbations are almost imperceptible to human eyes.
|
Use Monte carlo Tree Search and Homoglyphs to generate indistinguishable adversarial samples on text data
| 5
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
You will be given a definition of a task first, then some input of the task.
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Although deep neural networks show their extraordinary power in various tasks, they are not feasible for deploying such large models on embedded systems due to high computational cost and storage space limitation. The recent work knowledge distillation (KD) aims at transferring model knowledge from a well-trained teacher model to a small and fast student model which can significantly help extending the usage of large deep neural networks on portable platform. In this paper, we show that, by properly defining the neuron manifold of deep neuron network (DNN), we can significantly improve the performance of student DNN networks through approximating neuron manifold of powerful teacher network. To make this, we propose several novel methods for learning neuron manifold from DNN model. Empowered with neuron manifold knowledge, our experiments show the great improvement across a variety of DNN architectures and training data. Compared with other KD methods, our Neuron Manifold Transfer (NMT) has best transfer ability of the learned features.
Output:
|
A new knowledge distill method for transfer learning
| 1
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Teacher:In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Teacher: Now, understand the problem? Solve this instance: We study the problem of fitting task-specific learning rate schedules from the perspective of hyperparameter optimization. This allows us to explicitly search for schedules that achieve good generalization. We describe the structure of the gradient of a validation error w.r.t. the learning rates, the hypergradient, and based on this we introduce a novel online algorithm. Our method adaptively interpolates between two recently proposed techniques (Franceschi et al., 2017; Baydin et al.,2018), featuring increased stability and faster convergence. We show empirically that the proposed technique compares favorably with baselines and related methodsin terms of final test accuracy.
Student:
|
MARTHE: a new method to fit task-specific learning rate schedules from the perspective of hyperparameter optimization
| 6
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
You will be given a definition of a task first, then some input of the task.
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Training large deep neural networks on massive datasets is computationally very challenging. There has been recent surge in interest in using large batch stochastic optimization methods to tackle this issue. The most prominent algorithm in this line of research is LARS, which by employing layerwise adaptive learning rates trains ResNet on ImageNet in a few minutes. However, LARS performs poorly for attention models like BERT, indicating that its performance gains are not consistent across tasks. In this paper, we first study a principled layerwise adaptation strategy to accelerate training of deep neural networks using large mini-batches. Using this strategy, we develop a new layerwise adaptive large batch optimization technique called LAMB; we then provide convergence analysis of LAMB as well as LARS, showing convergence to a stationary point in general nonconvex settings. Our empirical results demonstrate the superior performance of LAMB across various tasks such as BERT and ResNet-50 training with very little hyperparameter tuning. In particular, for BERT training, our optimizer enables use of very large batch sizes of 32868 without any degradation of performance. By increasing the batch size to the memory limit of a TPUv3 Pod, BERT training time can be reduced from 3 days to just 76 minutes (Table 1).
Output:
|
A fast optimizer for general applications and large-batch training.
| 1
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Teacher:In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Teacher: Now, understand the problem? Solve this instance: Generative models forsource code are an interesting structured prediction problem, requiring to reason about both hard syntactic and semantic constraints as well as about natural, likely programs. We present a novel model for this problem that uses a graph to represent the intermediate state of the generated output. Our model generates code by interleaving grammar-driven expansion steps with graph augmentation and neural message passing steps. An experimental evaluation shows that our new model can generate semantically meaningful expressions, outperforming a range of strong baselines.
Student:
|
Representing programs as graphs including semantics helps when generating programs
| 6
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Input: We present EDA: easy data augmentation techniques for boosting performance on text classification tasks. EDA consists of four simple but powerful operations: synonym replacement, random insertion, random swap, and random deletion. On five text classification tasks, we show that EDA improves performance for both convolutional and recurrent neural networks. EDA demonstrates particularly strong results for smaller datasets; on average, across five datasets, training with EDA while using only 50% of the available training set achieved the same accuracy as normal training with all available data. We also performed extensive ablation studies and suggest parameters for practical use.
Output:
|
Simple text augmentation techniques can significantly boost performance on text classification tasks, especially for small datasets.
| 3
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Q: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Neural architecture search (NAS) has a great impact by automatically designing effective neural network architectures. However, the prohibitive computational demand of conventional NAS algorithms (e.g. 10 4 GPU hours) makes it difficult to directly search the architectures on large-scale tasks (e.g. ImageNet). Differentiable NAS can reduce the cost of GPU hours via a continuous representation of network architecture but suffers from the high GPU memory consumption issue (grow linearly w.r.t. candidate set size). As a result, they need to utilize proxy tasks, such as training on a smaller dataset, or learning with only a few blocks, or training just for a few epochs. These architectures optimized on proxy tasks are not guaranteed to be optimal on the target task. In this paper, we present ProxylessNAS that can directly learn the architectures for large-scale target tasks and target hardware platforms. We address the high memory consumption issue of differentiable NAS and reduce the computational cost (GPU hours and GPU memory) to the same level of regular training while still allowing a large candidate set. Experiments on CIFAR-10 and ImageNet demonstrate the effectiveness of directness and specialization. On CIFAR-10, our model achieves 2.08% test error with only 5.7M parameters, better than the previous state-of-the-art architecture AmoebaNet-B, while using 6× fewer parameters. On ImageNet, our model achieves 3.1% better top-1 accuracy than MobileNetV2, while being 1.2× faster with measured GPU latency. We also apply ProxylessNAS to specialize neural architectures for hardware with direct hardware metrics (e.g. latency) and provide insights for efficient CNN architecture design.
A:
|
Proxy-less neural architecture search for directly learning architectures on large-scale target task (ImageNet) while reducing the cost to the same level of normal training.
| 7
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
You will be given a definition of a task first, then some input of the task.
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Multi-domain learning (MDL) aims at obtaining a model with minimal average risk across multiple domains. Our empirical motivation is automated microscopy data, where cultured cells are imaged after being exposed to known and unknown chemical perturbations, and each dataset displays significant experimental bias. This paper presents a multi-domain adversarial learning approach, MuLANN, to leverage multiple datasets with overlapping but distinct class sets, in a semi-supervised setting. Our contributions include: i) a bound on the average- and worst-domain risk in MDL, obtained using the H-divergence; ii) a new loss to accommodate semi-supervised multi-domain learning and domain adaptation; iii) the experimental validation of the approach, improving on the state of the art on two standard image benchmarks, and a novel bioimage dataset, Cell.
Output:
|
Adversarial Domain adaptation and Multi-domain learning: a new loss to handle multi- and single-domain classes in the semi-supervised setting.
| 1
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Problem:The geometric properties of loss surfaces, such as the local flatness of a solution, are associated with generalization in deep learning. The Hessian is often used to understand these geometric properties. We investigate the differences between the eigenvalues of the neural network Hessian evaluated over the empirical dataset, the Empirical Hessian, and the eigenvalues of the Hessian under the data generating distribution, which we term the True Hessian. Under mild assumptions, we use random matrix theory to show that the True Hessian has eigenvalues of smaller absolute value than the Empirical Hessian. We support these results for different SGD schedules on both a 110-Layer ResNet and VGG-16. To perform these experiments we propose a framework for spectral visualization, based on GPU accelerated stochastic Lanczos quadrature. This approach is an order of magnitude faster than state-of-the-art methods for spectral visualization, and can be generically used to investigate the spectral properties of matrices in deep learning.
Solution:
|
Understanding the neural network Hessian eigenvalues under the data generating distribution.
| 8
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Input: A core aspect of human intelligence is the ability to learn new tasks quickly and switch between them flexibly. Here, we describe a modular continual reinforcement learning paradigm inspired by these abilities. We first introduce a visual interaction environment that allows many types of tasks to be unified in a single framework. We then describe a reward map prediction scheme that learns new tasks robustly in the very large state and action spaces required by such an environment. We investigate how properties of module architecture influence efficiency of task learning, showing that a module motif incorporating specific design principles (e.g. early bottlenecks, low-order polynomial nonlinearities, and symmetry) significantly outperforms more standard neural network motifs, needing fewer training examples and fewer neurons to achieve high levels of performance. Finally, we present a meta-controller architecture for task switching based on a dynamic neural voting scheme, which allows new modules to use information learned from previously-seen tasks to substantially improve their own learning efficiency.
Output:
|
We propose a neural module approach to continual learning using a unified visual environment with a large action space.
| 3
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Q: Building chatbots that can accomplish goals such as booking a flight ticket is an unsolved problem in natural language understanding. Much progress has been made to build conversation models using techniques such as sequence2sequence modeling. One challenge in applying such techniques to building goal-oriented conversation models is that maximum likelihood-based models are not optimized toward accomplishing goals. Recently, many methods have been proposed to address this issue by optimizing a reward that contains task status or outcome. However, adding the reward optimization on the fly usually provides little guidance for language construction and the conversation model soon becomes decoupled from the language model. In this paper, we propose a new setting in goal-oriented dialogue system to tighten the gap between these two aspects by enforcing model level information isolation on individual models between two agents. Language construction now becomes an important part in reward optimization since it is the only way information can be exchanged. We experimented our models using self-play and results showed that our method not only beat the baseline sequence2sequence model in rewards but can also generate human-readable meaningful conversations of comparable quality.
A:
|
A Goal-oriented Neural Conversation Model by Self-Play
| 4
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Problem:Training a model to perform a task typically requires a large amount of data from the domains in which the task will be applied.
However, it is often the case that data are abundant in some domains but scarce in others. Domain adaptation deals with the challenge of adapting a model trained from a data-rich source domain to perform well in a data-poor target domain. In general, this requires learning plausible mappings between domains. CycleGAN is a powerful framework that efficiently learns to map inputs from one domain to another using adversarial training and a cycle-consistency constraint. However, the conventional approach of enforcing cycle-consistency via reconstruction may be overly restrictive in cases where one or more domains have limited training data. In this paper, we propose an augmented cyclic adversarial learning model that enforces the cycle-consistency constraint via an external task specific model, which encourages the preservation of task-relevant content as opposed to exact reconstruction. We explore digit classification in a low-resource setting in supervised, semi and unsupervised situation, as well as high resource unsupervised. In low-resource supervised setting, the results show that our approach improves absolute performance by 14% and 4% when adapting SVHN to MNIST and vice versa, respectively, which outperforms unsupervised domain adaptation methods that require high-resource unlabeled target domain. Moreover, using only few unsupervised target data, our approach can still outperforms many high-resource unsupervised models. Our model also outperforms on USPS to MNIST and synthetic digit to SVHN for high resource unsupervised adaptation. In speech domains, we similarly adopt a speech recognition model from each domain as the task specific model. Our approach improves absolute performance of speech recognition by 2% for female speakers in the TIMIT dataset, where the majority of training samples are from male voices.
Solution:
|
A new cyclic adversarial learning augmented with auxiliary task model which improves domain adaptation performance in low resource supervised and unsupervised situations
| 8
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Q: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
To leverage crowd-sourced data to train multi-speaker text-to-speech (TTS) models that can synthesize clean speech for all speakers, it is essential to learn disentangled representations which can independently control the speaker identity and background noise in generated signals. However, learning such representations can be challenging, due to the lack of labels describing the recording conditions of each training example, and the fact that speakers and recording conditions are often correlated, e.g. since users often make many recordings using the same equipment. This paper proposes three components to address this problem by: (1) formulating a conditional generative model with factorized latent variables, (2) using data augmentation to add noise that is not correlated with speaker identity and whose label is known during training, and (3) using adversarial factorization to improve disentanglement. Experimental results demonstrate that the proposed method can disentangle speaker and noise attributes even if they are correlated in the training data, and can be used to consistently synthesize clean speech for all speakers. Ablation studies verify the importance of each proposed component.
A:
|
Data augmentation and adversarial training are very effective for disentangling correlated speaker and noise, enabling independent control of each attribute for text-to-speech synthesis.
| 7
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Q: Open-domain dialogue generation has gained increasing attention in Natural Language Processing. Comparing these methods requires a holistic means of dialogue evaluation. Human ratings are deemed as the gold standard. As human evaluation is inefficient and costly, an automated substitute is desirable. In this paper, we propose holistic evaluation metrics which capture both the quality and diversity of dialogues. Our metrics consists of (1) GPT-2 based context coherence between sentences in a dialogue, (2) GPT-2 based fluency in phrasing, and, (3) $n$-gram based diversity in responses to augmented queries. The empirical validity of our metrics is demonstrated by strong correlation with human judgments. We provide the associated code, datasets and human ratings.
A:
|
We propose automatic metrics to holistically evaluate open-dialogue generation and they strongly correlate with human evaluation.
| 9
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Given the task definition and input, reply with output. In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Many imaging tasks require global information about all pixels in an image. Conventional bottom-up classification networks globalize information by decreasing resolution; features are pooled and down-sampled into a single output. But for semantic segmentation and object detection tasks, a network must provide higher-resolution pixel-level outputs. To globalize information while preserving resolution, many researchers propose the inclusion of sophisticated auxiliary blocks, but these come at the cost of a considerable increase in network size and computational cost. This paper proposes stacked u-nets (SUNets), which iteratively combine features from different resolution scales while maintaining resolution. SUNets leverage the information globalization power of u-nets in a deeper net- work architectures that is capable of handling the complexity of natural images. SUNets perform extremely well on semantic segmentation tasks using a small number of parameters.
|
Presents new architecture which leverages information globalization power of u-nets in a deeper networks and performs well across tasks without any bells and whistles.
| 5
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Effectively capturing graph node sequences in the form of vector embeddings is critical to many applications. We achieve this by (i) first learning vector embeddings of single graph nodes and (ii) then composing them to compactly represent node sequences. Specifically, we propose SENSE-S (Semantically Enhanced Node Sequence Embedding - for Single nodes), a skip-gram based novel embedding mechanism, for single graph nodes that co-learns graph structure as well as their textual descriptions. We demonstrate that SENSE-S vectors increase the accuracy of multi-label classification tasks by up to 50% and link-prediction tasks by up to 78% under a variety of scenarios using real datasets. Based on SENSE-S, we next propose generic SENSE to compute composite vectors that represent a sequence of nodes, where preserving the node order is important. We prove that this approach is efficient in embedding node sequences, and our experiments on real data confirm its high accuracy in node order decoding.
|
Node sequence embedding mechanism that captures both graph and text properties.
| 0
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
You will be given a definition of a task first, then some input of the task.
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
The ability to forecast a set of likely yet diverse possible future behaviors of an agent (e.g., future trajectories of a pedestrian) is essential for safety-critical perception systems (e.g., autonomous vehicles). In particular, a set of possible future behaviors generated by the system must be diverse to account for all possible outcomes in order to take necessary safety precautions. It is not sufficient to maintain a set of the most likely future outcomes because the set may only contain perturbations of a dominating single outcome (major mode). While generative models such as variational autoencoders (VAEs) have been shown to be a powerful tool for learning a distribution over future trajectories, randomly drawn samples from the learned implicit likelihood model may not be diverse -- the likelihood model is derived from the training data distribution and the samples will concentrate around the major mode of the data. In this work, we propose to learn a diversity sampling function (DSF) that generates a diverse yet likely set of future trajectories. The DSF maps forecasting context features to a set of latent codes which can be decoded by a generative model (e.g., VAE) into a set of diverse trajectory samples. Concretely, the process of identifying the diverse set of samples is posed as DSF parameter estimation. To learn the parameters of the DSF, the diversity of the trajectory samples is evaluated by a diversity loss based on a determinantal point process (DPP). Gradient descent is performed over the DSF parameters, which in turn moves the latent codes of the sample set to find an optimal set of diverse yet likely trajectories. Our method is a novel application of DPPs to optimize a set of items (forecasted trajectories) in continuous space. We demonstrate the diversity of the trajectories produced by our approach on both low-dimensional 2D trajectory data and high-dimensional human motion data.
Output:
|
We learn a diversity sampling function with DPPs to obtain a diverse set of samples from a generative model.
| 1
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Input: Designing rewards for Reinforcement Learning (RL) is challenging because it needs to convey the desired task, be efficient to optimize, and be easy to compute. The latter is particularly problematic when applying RL to robotics, where detecting whether the desired configuration is reached might require considerable supervision and instrumentation. Furthermore, we are often interested in being able to reach a wide range of configurations, hence setting up a different reward every time might be unpractical. Methods like Hindsight Experience Replay (HER) have recently shown promise to learn policies able to reach many goals, without the need of a reward. Unfortunately, without tricks like resetting to points along the trajectory, HER might take a very long time to discover how to reach certain areas of the state-space. In this work we investigate different approaches to incorporate demonstrations to drastically speed up the convergence to a policy able to reach any goal, also surpassing the performance of an agent trained with other Imitation Learning algorithms. Furthermore, our method can be used when only trajectories without expert actions are available, which can leverage kinestetic or third person demonstration.
Output:
|
We tackle goal-conditioned tasks by combining Hindsight Experience Replay and Imitation Learning algorithms, showing faster convergence than the first and higher final performance than the second.
| 3
|
NIv2
|
task668_extreme_abstract_summarization
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zs_opt
|
Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Input: Machine translation is an important real-world application, and neural network-based AutoRegressive Translation (ART) models have achieved very promising accuracy. Due to the unparallelizable nature of the autoregressive factorization, ART models have to generate tokens one by one during decoding and thus suffer from high inference latency. Recently, Non-AutoRegressive Translation (NART) models were proposed to reduce the inference time. However, they could only achieve inferior accuracy compared with ART models. To improve the accuracy of NART models, in this paper, we propose to leverage the hints from a well-trained ART model to train the NART model. We define two hints for the machine translation task: hints from hidden states and hints from word alignments, and use such hints to regularize the optimization of NART models. Experimental results show that the NART model trained with hints could achieve significantly better translation performance than previous NART models on several tasks. In particular, for the WMT14 En-De and De-En task, we obtain BLEU scores of 25.20 and 29.52 respectively, which largely outperforms the previous non-autoregressive baselines. It is even comparable to a strong LSTM-based ART model (24.60 on WMT14 En-De), but one order of magnitude faster in inference.
Output:
|
We develop a training algorithm for non-autoregressive machine translation models, achieving comparable accuracy to strong autoregressive baselines, but one order of magnitude faster in inference.
| 3
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
We propose a framework to model the distribution of sequential data coming from
a set of entities connected in a graph with a known topology. The method is
based on a mixture of shared hidden Markov models (HMMs), which are trained
in order to exploit the knowledge of the graph structure and in such a way that the
obtained mixtures tend to be sparse. Experiments in different application domains
demonstrate the effectiveness and versatility of the method.
|
A method to model the generative distribution of sequences coming from graph connected entities.
| 0
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Q: The checkerboard phenomenon is one of the well-known visual artifacts in the computer vision field. The origins and solutions of checkerboard artifacts in the pixel space have been studied for a long time, but their effects on the gradient space have rarely been investigated. In this paper, we revisit the checkerboard artifacts in the gradient space which turn out to be the weak point of a network architecture. We explore image-agnostic property of gradient checkerboard artifacts and propose a simple yet effective defense method by utilizing the artifacts. We introduce our defense module, dubbed Artificial Checkerboard Enhancer (ACE), which induces adversarial attacks on designated pixels. This enables the model to deflect attacks by shifting only a single pixel in the image with a remarkable defense rate. We provide extensive experiments to support the effectiveness of our work for various attack scenarios using state-of-the-art attack methods. Furthermore, we show that ACE is even applicable to large-scale datasets including ImageNet dataset and can be easily transferred to various pretrained networks.
A:
|
We propose a novel aritificial checkerboard enhancer (ACE) module which guides attacks to a pre-specified pixel space and successfully defends it with a simple padding operation.
| 9
|
NIv2
|
task668_extreme_abstract_summarization
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zs_opt
|
Q: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Generative Adversarial Networks (GAN) can achieve promising performance on learning complex data distributions on different types of data. In this paper, we first show that a straightforward extension of an existing GAN algorithm is not applicable to point clouds, because the constraint required for discriminators is undefined for set data. We propose a two fold modification to a GAN algorithm to be able to generate point clouds (PC-GAN). First, we combine ideas from hierarchical Bayesian modeling and implicit generative models by learning a hierarchical and interpretable sampling process. A key component of our method is that we train a posterior inference network for the hidden variables. Second, PC-GAN defines a generic framework that can incorporate many existing GAN algorithms. We further propose a sandwiching objective, which results in a tighter Wasserstein distance estimate than the commonly used dual form in WGAN. We validate our claims on the ModelNet40 benchmark dataset and observe that PC- GAN trained by the sandwiching objective achieves better results on test data than existing methods. We also conduct studies on several tasks, including generalization on unseen point clouds, latent space interpolation, classification, and image to point clouds transformation, to demonstrate the versatility of the proposed PC-GAN algorithm.
A:
|
We propose a GAN variant which learns to generate point clouds. Different studies have been explores, including tighter Wasserstein distance estimate, conditional generation, generalization to unseen point clouds and image to point cloud.
| 7
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Q: We propose an interactive classification approach for natural language queries. Instead of classifying given the natural language query only, we ask the user for additional information using a sequence of binary and multiple-choice questions. At each turn, we use a policy controller to decide if to present a question or pro-vide the user the final answer, and select the best question to ask by maximizing the system information gain. Our formulation enables bootstrapping the system without any interaction data, instead relying on non-interactive crowdsourcing an-notation tasks. Our evaluation shows the interaction helps the system increase its accuracy and handle ambiguous queries, while our approach effectively balances the number of questions and the final accuracy.
A:
|
We propose an interactive approach for classifying natural language queries by asking users for additional information using information gain and a reinforcement learning policy controller.
| 4
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Q: With a growing number of available services, each having slightly different parameters, preconditions and effects, automated planning on general semantic services become highly relevant. However, most exiting planners only consider PDDL, or if they claim to use OWL-S, they usually translate it to PDDL, losing much of the semantics on the way.
In this paper, we propose a new domain-independent heuristic based on a semantic distance that can be used by generic planning algorithms such as A* for automated planning of semantic services described with OWL-S. For the heuristic to include more relevant information we calculate the heuristic at runtime. Using this heuristic, we are able to produce better results (fewer expanded states) in less time than with established techniques.
A:
|
Describing a semantic heuristics which builds upon an OWL-S service description and uses word and sentence distance measures to evaluate the usefulness of services for a given goal.
| 9
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Input: Deep neural networks have shown incredible performance for inference tasks in a variety of domains. Unfortunately, most current deep networks are enormous cloud-based structures that require significant storage space, which limits scaling of deep learning as a service (DLaaS) and use for on-device augmented intelligence. This paper finds algorithms that directly use lossless compressed representations of deep feedforward networks (with synaptic weights drawn from discrete sets), to perform inference without full decompression. The basic insight that allows less rate than naive approaches is the recognition that the bipartite graph layers of feedforward networks have a kind of permutation invariance to the labeling of nodes, in terms of inferential operation and that the inference operation depends locally on the edges directly connected to it. We also provide experimental results of our approach on the MNIST dataset.
Output:
|
This paper finds algorithms that directly use lossless compressed representations of deep feedforward networks, to perform inference without full decompression.
| 3
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Given the task definition and input, reply with output. In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Unsupervised and semi-supervised learning are important problems that are especially challenging with complex data like natural images. Progress on these problems would accelerate if we had access to appropriate generative models under which to pose the associated inference tasks. Inspired by the success of Convolutional Neural Networks (CNNs) for supervised prediction in images, we design the Neural Rendering Model (NRM), a new hierarchical probabilistic generative model whose inference calculations correspond to those in a CNN. The NRM introduces a small set of latent variables at each level of the model and enforces dependencies among all the latent variables via a conjugate prior distribution. The conjugate prior yields a new regularizer for learning based on the paths rendered in the generative model for training CNNs–the Rendering Path Normalization (RPN). We demonstrate that this regularizer improves generalization both in theory and in practice. Likelihood estimation in the NRM yields the new Max-Min cross entropy training loss, which suggests a new deep network architecture–the Max- Min network–which exceeds or matches the state-of-art for semi-supervised and supervised learning on SVHN, CIFAR10, and CIFAR100.
|
We develop a new deep generative model for semi-supervised learning and propose a new Max-Min cross-entropy for training CNNs.
| 5
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
You will be given a definition of a task first, then some input of the task.
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Generative models of natural images have progressed towards high fidelity samples by the strong leveraging of scale. We attempt to carry this success to the field of video modeling by showing that large Generative Adversarial Networks trained on the complex Kinetics-600 dataset are able to produce video samples of substantially higher complexity and fidelity than previous work. Our proposed model, Dual Video Discriminator GAN (DVD-GAN), scales to longer and higher resolution videos by leveraging a computationally efficient decomposition of its discriminator. We evaluate on the related tasks of video synthesis and video prediction, and achieve new state-of-the-art Fréchet Inception Distance for prediction for Kinetics-600, as well as state-of-the-art Inception Score for synthesis on the UCF-101 dataset, alongside establishing a strong baseline for synthesis on Kinetics-600.
Output:
|
We propose DVD-GAN, a large video generative model that is state of the art on several tasks and produces highly complex videos when trained on large real world datasets.
| 1
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NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Q: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Network quantization is a model compression and acceleration technique that has become essential to neural network deployment. Most quantization methods per- form fine-tuning on a pretrained network, but this sometimes results in a large loss in accuracy compared to the original network. We introduce a new technique to train quantization-friendly networks, which can be directly converted to an accurate quantized network without the need for additional fine-tuning. Our technique allows quantizing the weights and activations of all network layers down to 4 bits, achieving high efficiency and facilitating deployment in practical settings. Com- pared to other fully quantized networks operating at 4 bits, we show substantial improvements in accuracy, for example 66.68% top-1 accuracy on ImageNet using ResNet-18, compared to the previous state-of-the-art accuracy of 61.52% Louizos et al. (2019) and a full precision reference accuracy of 69.76%. We performed a thorough set of experiments to test the efficacy of our method and also conducted ablation studies on different aspects of the method and techniques to improve training stability and accuracy. Our codebase and trained models are available on GitHub.
A:
|
We train accurate fully quantized networks using a loss function maximizing full precision model accuracy and minimizing the difference between the full precision and quantized networks.
| 7
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Q: We evaluate the distribution learning capabilities of generative adversarial networks by testing them on synthetic datasets. The datasets include common distributions of points in $R^n$ space and images containing polygons of various shapes and sizes. We find that by and large GANs fail to faithfully recreate point datasets which contain discontinous support or sharp bends with noise. Additionally, on image datasets, we find that GANs do not seem to learn to count the number of objects of the same kind in an image. We also highlight the apparent tension between generalization and learning in GANs.
A:
|
GANs are evaluated on synthetic datasets
| 9
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Input: Generative modeling of high dimensional data like images is a notoriously difficult and ill-defined problem. In particular, how to evaluate a learned generative model is unclear.
In this paper, we argue that *adversarial learning*, pioneered with generative adversarial networks (GANs), provides an interesting framework to implicitly define more meaningful task losses for unsupervised tasks, such as for generating "visually realistic" images. By relating GANs and structured prediction under the framework of statistical decision theory, we put into light links between recent advances in structured prediction theory and the choice of the divergence in GANs. We argue that the insights about the notions of "hard" and "easy" to learn losses can be analogously extended to adversarial divergences. We also discuss the attractive properties of parametric adversarial divergences for generative modeling, and perform experiments to show the importance of choosing a divergence that reflects the final task.
Output:
|
Parametric adversarial divergences implicitly define more meaningful task losses for generative modeling, we make parallels with structured prediction to study the properties of these divergences and their ability to encode the task of interest.
| 3
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Q: Modern generative models are usually designed to match target distributions directly in the data space, where the intrinsic dimensionality of data can be much lower than the ambient dimensionality. We argue that this discrepancy may contribute to the difficulties in training generative models. We therefore propose to map both the generated and target distributions to the latent space using the encoder of a standard autoencoder, and train the generator (or decoder) to match the target distribution in the latent space. The resulting method, perceptual generative autoencoder (PGA), is then incorporated with maximum likelihood or variational autoencoder (VAE) objective to train the generative model. With maximum likelihood, PGA generalizes the idea of reversible generative models to unrestricted neural network architectures and arbitrary latent dimensionalities. When combined with VAE, PGA can generate sharper samples than vanilla VAE.
A:
|
A framework for training autoencoder-based generative models, with non-adversarial losses and unrestricted neural network architectures.
| 9
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Input: Representations learnt through deep neural networks tend to be highly informative, but opaque in terms of what information they learn to encode. We introduce an approach to probabilistic modelling that learns to represent data with two separate deep representations: an invariant representation that encodes the information of the class from which the data belongs, and an equivariant representation that encodes the symmetry transformation defining the particular data point within the class manifold (equivariant in the sense that the representation varies naturally with symmetry transformations). This approach to representation learning is conceptually transparent, easy to implement, and in-principle generally applicable to any data comprised of discrete classes of continuous distributions (e.g. objects in images, topics in language, individuals in behavioural data). We demonstrate qualitatively compelling representation learning and competitive quantitative performance, in both supervised and semi-supervised settings, versus comparable modelling approaches in the literature with little fine tuning.
Output:
|
This paper presents a novel latent-variable generative modelling technique that enables the representation of global information into one latent variable and local information into another latent variable.
| 3
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Q: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Deep image prior (DIP), which utilizes a deep convolutional network (ConvNet) structure itself as an image prior, has attracted huge attentions in computer vision community. It empirically shows the effectiveness of ConvNet structure for various image restoration applications. However, why the DIP works so well is still unknown, and why convolution operation is essential for image reconstruction or enhancement is not very clear. In this study, we tackle these questions. The proposed approach is dividing the convolution into ``delay-embedding'' and ``transformation (\ie encoder-decoder)'', and proposing a simple, but essential, image/tensor modeling method which is closely related to dynamical systems and self-similarity. The proposed method named as manifold modeling in embedded space (MMES) is implemented by using a novel denoising-auto-encoder in combination with multi-way delay-embedding transform. In spite of its simplicity, the image/tensor completion and super-resolution results of MMES are quite similar even competitive to DIP in our extensive experiments, and these results would help us for reinterpreting/characterizing the DIP from a perspective of ``low-dimensional patch-manifold prior''.
A:
|
We propose a new auto-encoder incorporated with multiway delay-embedding transform toward interpreting deep image prior.
| 7
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Given the task definition and input, reply with output. In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Whereas it is believed that techniques such as Adam, batch normalization and, more recently, SeLU nonlinearities ``solve'' the exploding gradient problem, we show that this is not the case and that in a range of popular MLP architectures, exploding gradients exist and that they limit the depth to which networks can be effectively trained, both in theory and in practice. We explain why exploding gradients occur and highlight the {\it collapsing domain problem}, which can arise in architectures that avoid exploding gradients.
ResNets have significantly lower gradients and thus can circumvent the exploding gradient problem, enabling the effective training of much deeper networks, which we show is a consequence of a surprising mathematical property. By noticing that {\it any neural network is a residual network}, we devise the {\it residual trick}, which reveals that introducing skip connections simplifies the network mathematically, and that this simplicity may be the major cause for their success.
|
We show that in contras to popular wisdom, the exploding gradient problem has not been solved and that it limits the depth to which MLPs can be effectively trained. We show why gradients explode and how ResNet handles them.
| 5
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NIv2
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task668_extreme_abstract_summarization
|
zs_opt
|
Teacher:In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Teacher: Now, understand the problem? Solve this instance: Classification systems typically act in isolation, meaning they are required to implicitly memorize the characteristics of all candidate classes in order to classify. The cost of this is increased memory usage and poor sample efficiency. We propose a model which instead verifies using reference images during the classification process, reducing the burden of memorization. The model uses iterative non-differentiable queries in order to classify an image. We demonstrate that such a model is feasible to train and can match baseline accuracy while being more parameter efficient. However, we show that finding the correct balance between image recognition and verification is essential to pushing the model towards desired behavior, suggesting that a pipeline of recognition followed by verification is a more promising approach towards designing more powerful networks with simpler architectures.
Student:
|
Image classification via iteratively querying for reference image from a candidate class with a RNN and use CNN to compare to the input image
| 6
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NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Q: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
In recent years, three-dimensional convolutional neural network (3D CNN) are intensively applied in the video analysis and action recognition and receives good performance. However, 3D CNN leads to massive computation and storage consumption, which hinders its deployment on mobile and embedded devices. In this paper, we propose a three-dimensional regularization-based pruning method to assign different regularization parameters to different weight groups based on their importance to the network. Our experiments show that the proposed method outperforms other popular methods in this area.
A:
|
In this paper, we propose a three-dimensional regularization-based pruning method to accelerate the 3D-CNN.
| 7
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NIv2
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task668_extreme_abstract_summarization
|
zs_opt
|
Definition: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Input: Methods that align distributions by minimizing an adversarial distance between them have recently achieved impressive results. However, these approaches are difficult to optimize with gradient descent and they often do not converge well without careful hyperparameter tuning and proper initialization. We investigate whether turning the adversarial min-max problem into an optimization problem by replacing the maximization part with its dual improves the quality of the resulting alignment and explore its connections to Maximum Mean Discrepancy. Our empirical results suggest that using the dual formulation for the restricted family of linear discriminators results in a more stable convergence to a desirable solution when compared with the performance of a primal min-max GAN-like objective and an MMD objective under the same restrictions. We test our hypothesis on the problem of aligning two synthetic point clouds on a plane and on a real-image domain adaptation problem on digits. In both cases, the dual formulation yields an iterative procedure that gives more stable and monotonic improvement over time.
Output:
|
We propose a dual version of the logistic adversarial distance for feature alignment and show that it yields more stable gradient step iterations than the min-max objective.
| 2
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NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Teacher:In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Teacher: Now, understand the problem? Solve this instance: Efficient exploration remains a major challenge for reinforcement learning. One reason is that the variability of the returns often depends on the current state and action, and is therefore heteroscedastic. Classical exploration strategies such as upper confidence bound algorithms and Thompson sampling fail to appropriately account for heteroscedasticity, even in the bandit setting. Motivated by recent findings that address this issue in bandits, we propose to use Information-Directed Sampling (IDS) for exploration in reinforcement learning. As our main contribution, we build on recent advances in distributional reinforcement learning and propose a novel, tractable approximation of IDS for deep Q-learning. The resulting exploration strategy explicitly accounts for both parametric uncertainty and heteroscedastic observation noise. We evaluate our method on Atari games and demonstrate a significant improvement over alternative approaches.
Student:
|
We develop a practical extension of Information-Directed Sampling for Reinforcement Learning, which accounts for parametric uncertainty and heteroscedasticity in the return distribution for exploration.
| 6
|
NIv2
|
task668_extreme_abstract_summarization
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zs_opt
|
Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Problem:In many environments only a tiny subset of all states yield high reward. In these cases, few of the interactions with the environment provide a relevant learning signal. Hence, we may want to preferentially train on those high-reward states and the probable trajectories leading to them.
To this end, we advocate for the use of a \textit{backtracking model} that predicts the preceding states that terminate at a given high-reward state. We can train a model which, starting from a high value state (or one that is estimated to have high value), predicts and samples which (state, action)-tuples may have led to that high value state. These traces of (state, action) pairs, which we refer to as Recall Traces, sampled from this backtracking model starting from a high value state, are informative as they terminate in good states, and hence we can use these traces to improve a policy. We provide a variational interpretation for this idea and a practical algorithm in which the backtracking model samples from an approximate posterior distribution over trajectories which lead to large rewards. Our method improves the sample efficiency of both on- and off-policy RL algorithms across several environments and tasks.
Solution:
|
A backward model of previous (state, action) given the next state, i.e. P(s_t, a_t | s_{t+1}), can be used to simulate additional trajectories terminating at states of interest! Improves RL learning efficiency.
| 8
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NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Reinforcement learning algorithms, though successful, tend to over-fit to training environments, thereby hampering their application to the real-world. This paper proposes $\text{W}\text{R}^{2}\text{L}$ -- a robust reinforcement learning algorithm with significant robust performance on low and high-dimensional control tasks. Our method formalises robust reinforcement learning as a novel min-max game with a Wasserstein constraint for a correct and convergent solver. Apart from the formulation, we also propose an efficient and scalable solver following a novel zero-order optimisation method that we believe can be useful to numerical optimisation in general.
We empirically demonstrate significant gains compared to standard and robust state-of-the-art algorithms on high-dimensional MuJuCo environments
|
An RL algorithm that learns to be robust to changes in dynamics
| 0
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
|
Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Problem:Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more important role of image textures. We here put these conflicting hypotheses to a quantitative test by evaluating CNNs and human observers on images with a texture-shape cue conflict. We show that ImageNet-trained CNNs are strongly biased towards recognising textures rather than shapes, which is in stark contrast to human behavioural evidence and reveals fundamentally different classification strategies. We then demonstrate that the same standard architecture (ResNet-50) that learns a texture-based representation on ImageNet is able to learn a shape-based representation instead when trained on 'Stylized-ImageNet', a stylized version of ImageNet. This provides a much better fit for human behavioural performance in our well-controlled psychophysical lab setting (nine experiments totalling 48,560 psychophysical trials across 97 observers) and comes with a number of unexpected emergent benefits such as improved object detection performance and previously unseen robustness towards a wide range of image distortions, highlighting advantages of a shape-based representation.
Solution:
|
ImageNet-trained CNNs are biased towards object texture (instead of shape like humans). Overcoming this major difference between human and machine vision yields improved detection performance and previously unseen robustness to image distortions.
| 8
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NIv2
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task668_extreme_abstract_summarization
|
zs_opt
|
Q: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Network embedding (NE) methods aim to learn low-dimensional representations of network nodes as vectors, typically in Euclidean space. These representations are then used for a variety of downstream prediction tasks. Link prediction is one of the most popular choices for assessing the performance of NE methods. However, the complexity of link prediction requires a carefully designed evaluation pipeline to provide consistent, reproducible and comparable results. We argue this has not been considered sufficiently in recent works. The main goal of this paper is to overcome difficulties associated with evaluation pipelines and reproducibility of results. We introduce EvalNE, an evaluation framework to transparently assess and compare the performance of NE methods on link prediction. EvalNE provides automation and abstraction for tasks such as hyper-parameter tuning, model validation, edge sampling, computation of edge embeddings and model validation. The framework integrates efficient procedures for edge and non-edge sampling and can be used to easily evaluate any off-the-shelf embedding method. The framework is freely available as a Python toolbox. Finally, demonstrating the usefulness of EvalNE in practice, we conduct an empirical study in which we try to replicate and analyse experimental sections of several influential papers.
A:
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In this paper we introduce EvalNE, a Python toolbox for automating the evaluation of network embedding methods on link prediction and ensuring the reproducibility of results.
| 7
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NIv2
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task668_extreme_abstract_summarization
|
zs_opt
|
Definition: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Input: The peripheral nervous system represents the input/output system for the brain. Cuff electrodes implanted on the peripheral nervous system allow observation and control over this system, however, the data produced by these electrodes have a low signal-to-noise ratio and a complex signal content. In this paper, we consider the analysis of neural data recorded from the vagus nerve in animal models, and develop an unsupervised learner based on convolutional neural networks that is able to simultaneously de-noise and cluster regions of the data by signal content.
Output:
|
Unsupervised analysis of data recorded from the peripheral nervous system denoises and categorises signals.
| 2
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
You will be given a definition of a task first, then some input of the task.
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
This paper considers multi-agent reinforcement learning (MARL) in networked system control. Specifically, each agent learns a decentralized control policy based on local observations and messages from connected neighbors. We formulate such a networked MARL (NMARL) problem as a spatiotemporal Markov decision process and introduce a spatial discount factor to stabilize the training of each local agent. Further, we propose a new differentiable communication protocol, called NeurComm, to reduce information loss and non-stationarity in NMARL. Based on experiments in realistic NMARL scenarios of adaptive traffic signal control and cooperative adaptive cruise control, an appropriate spatial discount factor effectively enhances the learning curves of non-communicative MARL algorithms, while NeurComm outperforms existing communication protocols in both learning efficiency and control performance.
Output:
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This paper proposes a new formulation and a new communication protocol for networked multi-agent control problems
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Q: The field of few-shot learning has recently seen substantial advancements. Most of these advancements came from casting few-shot learning as a meta-learning problem.Model Agnostic Meta Learning or MAML is currently one of the best approaches for few-shot learning via meta-learning. MAML is simple, elegant and very powerful, however, it has a variety of issues, such as being very sensitive to neural network architectures, often leading to instability during training, requiring arduous hyperparameter searches to stabilize training and achieve high generalization and being very computationally expensive at both training and inference times. In this paper, we propose various modifications to MAML that not only stabilize the system, but also substantially improve the generalization performance, convergence speed and computational overhead of MAML, which we call MAML++.
A:
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MAML is great, but it has many problems, we solve many of those problems and as a result we learn most hyper parameters end to end, speed-up training and inference and set a new SOTA in few-shot learning
| 9
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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Given the task definition and input, reply with output. In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
The goal of generative models is to model the underlying data distribution of a
sample based dataset. Our intuition is that an accurate model should in principle
also include the sample based dataset as part of its induced probability distribution.
To investigate this, we look at fully trained generative models using the Generative
Adversarial Networks (GAN) framework and analyze the resulting generator
on its ability to memorize the dataset. Further, we show that the size of the initial
latent space is paramount to allow for an accurate reconstruction of the training
data. This gives us a link to compression theory, where Autoencoders (AE) are
used to lower bound the reconstruction capabilities of our generative model. Here,
we observe similar results to the perception-distortion tradeoff (Blau & Michaeli
(2018)). Given a small latent space, the AE produces low quality and the GAN
produces high quality outputs from a perceptual viewpoint. In contrast, the distortion
error is smaller for the AE. By increasing the dimensionality of the latent
space the distortion decreases for both models, but the perceptual quality only
increases for the AE.
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We analyze the impact of the latent space of fully trained generators by pseudo inverting them.
| 5
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
The exposure bias problem refers to the training-inference discrepancy caused by teacher forcing in maximum likelihood estimation (MLE) training for auto-regressive neural network language models (LM). It has been regarded as a central problem for natural language generation (NLG) model training. Although a lot of algorithms have been proposed to avoid teacher forcing and therefore to alleviate exposure bias, there is little work showing how serious the exposure bias problem is. In this work, we first identify the auto-recovery ability of MLE-trained LM, which casts doubt on the seriousness of exposure bias. We then develop a precise, quantifiable definition for exposure bias. However, according to our measurements in controlled experiments, there's only around 3% performance gain when the training-inference discrepancy is completely removed. Our results suggest the exposure bias problem could be much less serious than it is currently assumed to be.
|
We show that exposure bias could be much less serious than it is currently assumed to be for MLE LM training.
| 0
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in Monte Carlo VI for neural networks. The framework can successfully train both deep discriminative models and deep generative models in complex continual learning settings where existing tasks evolve over time and entirely new tasks emerge. Experimental results show that VCL outperforms state-of-the-art continual learning methods on a variety of tasks, avoiding catastrophic forgetting in a fully automatic way.
|
This paper develops a principled method for continual learning in deep models.
| 0
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Q: System identification is the process of building a mathematical model of an unknown system from measurements of its inputs and outputs. It is a key step for model-based control, estimator design, and output prediction. This work presents an algorithm for non-linear offline system identification from partial observations, i.e. situations in which the system's full-state is not directly observable. The algorithm presented, called SISL, iteratively infers the system's full state through non-linear optimization and then updates the model parameters. We test our algorithm on a simulated system of coupled Lorenz attractors, showing our algorithm's ability to identify high-dimensional systems that prove intractable for particle-based approaches. We also use SISL to identify the dynamics of an aerobatic helicopter. By augmenting the state with unobserved fluid states, we learn a model that predicts the acceleration of the helicopter better than state-of-the-art approaches.
A:
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This work presents a scalable algorithm for non-linear offline system identification from partial observations.
| 9
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Input: Very recently, it comes to be a popular approach for answering open-domain questions by first searching question-related passages, then applying reading comprehension models to extract answers. Existing works usually extract answers from single passages independently, thus not fully make use of the multiple searched passages, especially for the some questions requiring several evidences, which can appear in different passages, to be answered. The above observations raise the problem of evidence aggregation from multiple passages. In this paper, we deal with this problem as answer re-ranking. Specifically, based on the answer candidates generated from the existing state-of-the-art QA model, we propose two different re-ranking methods, strength-based and coverage-based re-rankers, which make use of the aggregated evidences from different passages to help entail the ground-truth answer for the question. Our model achieved state-of-the-arts on three public open-domain QA datasets, Quasar-T, SearchQA and the open-domain version of TriviaQA, with about 8\% improvement on the former two datasets.
Output:
|
We propose a method that can make use of the multiple passages information for open-domain QA.
| 3
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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You will be given a definition of a task first, then some input of the task.
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Unsupervised domain adaptation is a promising avenue to enhance the performance of deep neural networks on a target domain, using labels only from a source domain. However, the two predominant methods, domain discrepancy reduction learning and semi-supervised learning, are not readily applicable when source and target domains do not share a common label space. This paper addresses the above scenario by learning a representation space that retains discriminative power on both the (labeled) source and (unlabeled) target domains while keeping representations for the two domains well-separated. Inspired by a theoretical analysis, we first reformulate the disjoint classification task, where the source and target domains correspond to non-overlapping class labels, to a verification one. To handle both within and cross domain verifications, we propose a Feature Transfer Network (FTN) to separate the target feature space from the original source space while aligned with a transformed source space. Moreover, we present a non-parametric multi-class entropy minimization loss to further boost the discriminative power of FTNs on the target domain. In experiments, we first illustrate how FTN works in a controlled setting of adapting from MNIST-M to MNIST with disjoint digit classes between the two domains and then demonstrate the effectiveness of FTNs through state-of-the-art performances on a cross-ethnicity face recognition problem.
Output:
|
A new theory of unsupervised domain adaptation for distance metric learning and its application to face recognition across diverse ethnicity variations.
| 1
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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Definition: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Input: Plan recognition aims to look for target plans to best explain the observed actions based on plan libraries and/or domain models. Despite the success of previous approaches on plan recognition, they mostly rely on correct action observations.
Recent advances in visual activity recognition have the potential of enabling applications such as automated video surveillance. Effective approaches for such problems would require the ability to recognize the plans of agents from video information. Traditional plan recognition algorithms rely on access to detailed planning domain models. One recent promising direction involves learning approximate (or shallow) domain models directly from the observed activity sequences. Such plan recognition approaches expect observed action sequences as inputs. However, visual inference results are often noisy and uncertain, typically represented as a distribution over possible actions. In this work, we develop a visual plan recognition framework that recognizes plans with an approximate domain model learned from uncertain visual data.
Output:
|
Handling Uncertainty in Visual Perception for Plan Recognition
| 2
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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Q: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e.g., following natural language instructions or dialog. However, existing methods tend to overfit training data in seen environments and fail to generalize well in previously unseen environments. In order to close the gap between seen and unseen environments, we aim at learning a generalizable navigation model from two novel perspectives:
(1) we introduce a multitask navigation model that can be seamlessly trained on both Vision-Language Navigation (VLN) and Navigation from Dialog History (NDH) tasks, which benefits from richer natural language guidance and effectively transfers knowledge across tasks;
(2) we propose to learn environment-agnostic representations for navigation policy that are invariant among environments, thus generalizing better on unseen environments.
Extensive experiments show that our environment-agnostic multitask navigation model significantly reduces the performance gap between seen and unseen environments and outperforms the baselines on unseen environments by 16% (relative measure on success rate) on VLN and 120% (goal progress) on NDH, establishing the new state of the art for NDH task.
A:
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We propose to learn a more generalized policy for natural language grounded navigation tasks via environment-agnostic multitask learning.
| 7
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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Teacher:In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Teacher: Now, understand the problem? Solve this instance: The domain of time-series forecasting has been extensively studied because it is of fundamental importance in many real-life applications. Weather prediction, traffic flow forecasting or sales are compelling examples of sequential phenomena. Predictive models generally make use of the relations between past and future values. However, in the case of stationary time-series, observed values also drastically depend on a number of exogenous features that can be used to improve forecasting quality. In this work, we propose a change of paradigm which consists in learning such features in embeddings vectors within recurrent neural networks. We apply our framework to forecast smart cards tap-in logs in the Parisian subway network. Results show that context-embedded models perform quantitatively better in one-step ahead and multi-step ahead forecasting.
Student:
|
In order to forecast multivariate stationary time-series we learn embeddings containing contextual features within a RNN; we apply the framework on public transportation data
| 6
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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You will be given a definition of a task first, then some input of the task.
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Convolutional architectures have recently been shown to be competitive on many
sequence modelling tasks when compared to the de-facto standard of recurrent neural networks (RNNs) while providing computational and modelling advantages due to inherent parallelism. However, currently, there remains a performance
gap to more expressive stochastic RNN variants, especially those with several layers of dependent random variables. In this work, we propose stochastic temporal convolutional networks (STCNs), a novel architecture that combines the computational advantages of temporal convolutional networks (TCN) with the representational power and robustness of stochastic latent spaces. In particular, we propose a hierarchy of stochastic latent variables that captures temporal dependencies at different time-scales. The architecture is modular and flexible due to the decoupling of the deterministic and stochastic layers. We show that the proposed architecture achieves state of the art log-likelihoods across several tasks. Finally, the model is capable of predicting high-quality synthetic samples over a long-range temporal horizon in modelling of handwritten text.
Output:
|
We combine the computational advantages of temporal convolutional architectures with the expressiveness of stochastic latent variables.
| 1
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Q: This work presents a scalable solution to continuous visual speech recognition. To achieve this, we constructed the largest existing visual speech recognition dataset, consisting of pairs of text and video clips of faces speaking (3,886 hours of video). In tandem, we designed and trained an integrated lipreading system, consisting of a video processing pipeline that maps raw video to stable videos of lips and sequences of phonemes, a scalable deep neural network that maps the lip videos to sequences of phoneme distributions, and a production-level speech decoder that outputs sequences of words. The proposed system achieves a word error rate (WER) of 40.9% as measured on a held-out set. In comparison, professional lipreaders achieve either 86.4% or 92.9% WER on the same dataset when having access to additional types of contextual information. Our approach significantly improves on previous lipreading approaches, including variants of LipNet and of Watch, Attend, and Spell (WAS), which are only capable of 89.8% and 76.8% WER respectively.
A:
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This work presents a scalable solution to continuous visual speech recognition.
| 9
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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Teacher:In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Teacher: Now, understand the problem? Solve this instance: What can we learn about the functional organization of cortical microcircuits from large-scale recordings of neural activity? To obtain an explicit and interpretable model of time-dependent functional connections between neurons and to establish the dynamics of the cortical information flow, we develop 'dynamic neural relational inference' (dNRI). We study both synthetic and real-world neural spiking data and demonstrate that the developed method is able to uncover the dynamic relations between neurons more reliably than existing baselines.
Student:
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We develop 'dynamic neural relational inference', a variational autoencoder model that can explicitly and interpretably represent the hidden dynamic relations between neurons.
| 6
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Q: Most research on lifelong learning applies to images or games, but not language.
We present LAMOL, a simple yet effective method for lifelong language learning (LLL) based on language modeling.
LAMOL replays pseudo-samples of previous tasks while requiring no extra memory or model capacity.
Specifically, LAMOL is a language model that simultaneously learns to solve the tasks and generate training samples.
When the model is trained for a new task, it generates pseudo-samples of previous tasks for training alongside data for the new task.
The results show that LAMOL prevents catastrophic forgetting without any sign of intransigence and can perform five very different language tasks sequentially with only one model.
Overall, LAMOL outperforms previous methods by a considerable margin and is only 2-3% worse than multitasking, which is usually considered the LLL upper bound.
The source code is available at https://github.com/jojotenya/LAMOL.
A:
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Language modeling for lifelong language learning.
| 4
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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Teacher:In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Teacher: Now, understand the problem? Solve this instance: Reinforcement learning provides a powerful and general framework for decision
making and control, but its application in practice is often hindered by the need
for extensive feature and reward engineering. Deep reinforcement learning methods
can remove the need for explicit engineering of policy or value features, but
still require a manually specified reward function. Inverse reinforcement learning
holds the promise of automatic reward acquisition, but has proven exceptionally
difficult to apply to large, high-dimensional problems with unknown dynamics. In
this work, we propose AIRL, a practical and scalable inverse reinforcement learning
algorithm based on an adversarial reward learning formulation that is competitive
with direct imitation learning algorithms. Additionally, we show that AIRL is
able to recover portable reward functions that are robust to changes in dynamics,
enabling us to learn policies even under significant variation in the environment
seen during training.
Student:
|
We propose an adversarial inverse reinforcement learning algorithm capable of learning reward functions which can transfer to new, unseen environments.
| 6
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Q: When machine learning models are used for high-stakes decisions, they should predict accurately, fairly, and responsibly. To fulfill these three requirements, a model must be able to output a reject option (i.e. say "``I Don't Know") when it is not qualified to make a prediction. In this work, we propose learning to defer, a method by which a model can defer judgment to a downstream decision-maker such as a human user. We show that learning to defer generalizes the rejection learning framework in two ways: by considering the effect of other agents in the decision-making process, and by allowing for optimization of complex objectives. We propose a learning algorithm which accounts for potential biases held by decision-makerslater in a pipeline. Experiments on real-world datasets demonstrate that learning
to defer can make a model not only more accurate but also less biased. Even when
operated by highly biased users, we show that
deferring models can still greatly improve the fairness of the entire pipeline.
A:
|
Incorporating the ability to say I-don't-know can improve the fairness of a classifier without sacrificing too much accuracy, and this improvement magnifies when the classifier has insight into downstream decision-making.
| 9
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Q: In many applications, it is desirable to extract only the relevant information from complex input data, which involves making a decision about which input features are relevant.
The information bottleneck method formalizes this as an information-theoretic optimization problem by maintaining an optimal tradeoff between compression (throwing away irrelevant input information), and predicting the target. In many problem settings, including the reinforcement learning problems we consider in this work, we might prefer to compress only part of the input. This is typically the case when we have a standard conditioning input, such as a state observation, and a ``privileged'' input, which might correspond to the goal of a task, the output of a costly planning algorithm, or communication with another agent. In such cases, we might prefer to compress the privileged input, either to achieve better generalization (e.g., with respect to goals) or to minimize access to costly information (e.g., in the case of communication). Practical implementations of the information bottleneck based on variational inference require access to the privileged input in order to compute the bottleneck variable, so although they perform compression, this compression operation itself needs unrestricted, lossless access. In this work, we propose the variational bandwidth bottleneck, which decides for each example on the estimated value of the privileged information before seeing it, i.e., only based on the standard input, and then accordingly chooses stochastically, whether to access the privileged input or not. We formulate a tractable approximation to this framework and demonstrate in a series of reinforcement learning experiments that it can improve generalization and reduce access to computationally costly information.
A:
|
Training agents with adaptive computation based on information bottleneck can promote generalization.
| 9
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Input: Abstraction of Markov Decision Processes is a useful tool for solving complex problems, as it can ignore unimportant aspects of an environment, simplifying the process of learning an optimal policy. In this paper, we propose a new algorithm for finding abstract MDPs in environments with continuous state spaces. It is based on MDP homomorphisms, a structure-preserving mapping between MDPs. We demonstrate our algorithm's ability to learns abstractions from collected experience and show how to reuse the abstractions to guide exploration in new tasks the agent encounters. Our novel task transfer method beats a baseline based on a deep Q-network.
Output:
|
We create abstract models of environments from experience and use them to learn new tasks faster.
| 3
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Q: Single cell RNA sequencing (scRNAseq) technology enables quantifying gene expression profiles by individual cells within cancer. Dimension reduction methods have been commonly used for cell clustering analysis and visualization of the data. Current dimension reduction methods tend overly eliminate the expression variations correspond to less dominating characteristics, such we fail to find the homogenious properties of cancer development. In this paper, we proposed a new and clustering analysis method for scRNAseq data, namely BBSC, via implementing a binarization of the gene expression profile into on/off frequency changes with a Boolean matrix factorization. The low rank representation of expression matrix recovered by BBSC increase the resolution in identifying distinct cell types or functions. Application of BBSC on two cancer scRNAseq data successfully discovered both homogeneous and heterogeneous cancer cell clusters. Further finding showed potential in preventing cancer progression.
A:
|
Our finding shed lights in preventing cancer progression
| 4
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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Teacher:In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Teacher: Now, understand the problem? Solve this instance: Learning representations of data is an important issue in machine learning. Though GAN has led to significant improvements in the data representations, it still has several problems such as unstable training, hidden manifold of data, and huge computational overhead. GAN tends to produce the data simply without any information about the manifold of the data, which hinders from controlling desired features to generate. Moreover, most of GAN’s have a large size of manifold, resulting in poor scalability. In this paper, we propose a novel GAN to control the latent semantic representation, called LSC-GAN, which allows us to produce desired data to generate and learns a representation of the data efficiently. Unlike the conventional GAN models with hidden distribution of latent space, we define the distributions explicitly in advance that are trained to generate the data based on the corresponding features by inputting the latent variables that follow the distribution. As the larger scale of latent space caused by deploying various distributions in one latent space makes training unstable while maintaining the dimension of latent space, we need to separate the process of defining the distributions explicitly and operation of generation. We prove that a VAE is proper for the former and modify a loss function of VAE to map the data into the pre-defined latent space so as to locate the reconstructed data as close to the input data according to its characteristics. Moreover, we add the KL divergence to the loss function of LSC-GAN to include this process. The decoder of VAE, which generates the data with the corresponding features from the pre-defined latent space, is used as the generator of the LSC-GAN. Several experiments on the CelebA dataset are conducted to verify the usefulness of the proposed method to generate desired data stably and efficiently, achieving a high compression ratio that can hold about 24 pixels of information in each dimension of latent space. Besides, our model learns the reverse of features such as not laughing (rather frowning) only with data of ordinary and smiling facial expression.
Student:
|
We propose a generative model that not only produces data with desired features from the pre-defined latent space but also fully understands the features of the data to create characteristics that are not in the dataset.
| 6
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
The main goal of network pruning is imposing sparsity on the neural network by increasing the number of parameters with zero value in order to reduce the architecture size and the computational speedup.
|
Proposing a novel method based on the guided attention to enforce the sparisty in deep neural networks.
| 0
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Input: Prospection is an important part of how humans come up with new task plans, but has not been explored in depth in robotics. Predicting multiple task-level is a challenging problem that involves capturing both task semantics and continuous variability over the state of the world. Ideally, we would combine the ability of machine learning to leverage big data for learning the semantics of a task, while using techniques from task planning to reliably generalize to new environment. In this work, we propose a method for learning a model encoding just such a representation for task planning. We learn a neural net that encodes the k most likely outcomes from high level actions from a given world. Our approach creates comprehensible task plans that allow us to predict changes to the environment many time steps into the future. We demonstrate this approach via application to a stacking task in a cluttered environment, where the robot must select between different colored blocks while avoiding obstacles, in order to perform a task. We also show results on a simple navigation task. Our algorithm generates realistic image and pose predictions at multiple points in a given task.
Output:
|
We describe an architecture for generating diverse hypotheses for intermediate goals during robotic manipulation tasks.
| 3
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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Given the task definition and input, reply with output. In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Despite its empirical success, the theoretical underpinnings of the stability, convergence and acceleration properties of batch normalization (BN) remain elusive. In this paper, we attack this problem from a modelling approach, where we perform thorough theoretical analysis on BN applied to simplified model: ordinary least squares (OLS). We discover that gradient descent on OLS with BN has interesting properties, including a scaling law, convergence for arbitrary learning rates for the weights, asymptotic acceleration effects, as well as insensitivity to choice of learning rates. We then demonstrate numerically that these findings are not specific to the OLS problem and hold qualitatively for more complex supervised learning problems. This points to a new direction towards uncovering the mathematical principles that underlies batch normalization.
|
We mathematically analyze the effect of batch normalization on a simple model and obtain key new insights that applies to general supervised learning.
| 5
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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Q: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Unsupervised domain adaptation has received significant attention in recent years. Most of existing works tackle the closed-set scenario, assuming that the source and target domains share the exactly same categories. In practice, nevertheless, a target domain often contains samples of classes unseen in source domain (i.e., unknown class). The extension of domain adaptation from closed-set to such open-set situation is not trivial since the target samples in unknown class are not expected to align with the source. In this paper, we address this problem by augmenting the state-of-the-art domain adaptation technique, Self-Ensembling, with category-agnostic clusters in target domain. Specifically, we present Self-Ensembling with Category-agnostic Clusters (SE-CC) --- a novel architecture that steers domain adaptation with the additional guidance of category-agnostic clusters that are specific to target domain. These clustering information provides domain-specific visual cues, facilitating the generalization of Self-Ensembling for both closed-set and open-set scenarios. Technically, clustering is firstly performed over all the unlabeled target samples to obtain the category-agnostic clusters, which reveal the underlying data space structure peculiar to target domain. A clustering branch is capitalized on to ensure that the learnt representation preserves such underlying structure by matching the estimated assignment distribution over clusters to the inherent cluster distribution for each target sample. Furthermore, SE-CC enhances the learnt representation with mutual information maximization. Extensive experiments are conducted on Office and VisDA datasets for both open-set and closed-set domain adaptation, and superior results are reported when comparing to the state-of-the-art approaches.
A:
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We present a new design, i.e., Self-Ensembling with Category-agnostic Clusters, for both closed-set and open-set domain adaptation.
| 7
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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Q: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
We introduce "Search with Amortized Value Estimates" (SAVE), an approach for combining model-free Q-learning with model-based Monte-Carlo Tree Search (MCTS). In SAVE, a learned prior over state-action values is used to guide MCTS, which estimates an improved set of state-action values. The new Q-estimates are then used in combination with real experience to update the prior. This effectively amortizes the value computation performed by MCTS, resulting in a cooperative relationship between model-free learning and model-based search. SAVE can be implemented on top of any Q-learning agent with access to a model, which we demonstrate by incorporating it into agents that perform challenging physical reasoning tasks and Atari. SAVE consistently achieves higher rewards with fewer training steps, and---in contrast to typical model-based search approaches---yields strong performance with very small search budgets. By combining real experience with information computed during search, SAVE demonstrates that it is possible to improve on both the performance of model-free learning and the computational cost of planning.
A:
|
We propose a model-based method called "Search with Amortized Value Estimates" (SAVE) which leverages both real and planned experience by combining Q-learning with Monte-Carlo Tree Search, achieving strong performance with very small search budgets.
| 7
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NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
One of the long-standing challenges in Artificial Intelligence for learning goal-directed behavior is to build a single agent which can solve multiple tasks. Recent progress in multi-task learning for goal-directed sequential problems has been in the form of distillation based learning wherein a student network learns from multiple task-specific expert networks by mimicking the task-specific policies of the expert networks. While such approaches offer a promising solution to the multi-task learning problem, they require supervision from large expert networks which require extensive data and computation time for training.
In this work, we propose an efficient multi-task learning framework which solves multiple goal-directed tasks in an on-line setup without the need for expert supervision. Our work uses active learning principles to achieve multi-task learning by sampling the harder tasks more than the easier ones. We propose three distinct models under our active sampling framework. An adaptive method with extremely competitive multi-tasking performance. A UCB-based meta-learner which casts the problem of picking the next task to train on as a multi-armed bandit problem. A meta-learning method that casts the next-task picking problem as a full Reinforcement Learning problem and uses actor-critic methods for optimizing the multi-tasking performance directly. We demonstrate results in the Atari 2600 domain on seven multi-tasking instances: three 6-task instances, one 8-task instance, two 12-task instances and one 21-task instance.
|
Letting a meta-learner decide the task to train on for an agent in a multi-task setting improves multi-tasking ability substantially
| 0
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Given the task definition and input, reply with output. In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Electronic Health Records (EHR) comprise of longitudinal clinical observations portrayed with sparsity, irregularity, and high-dimensionality which become the major obstacles in drawing reliable downstream outcome. Despite greatly numbers of imputation methods are being proposed to tackle these issues, most of the existing methods ignore correlated features or temporal dynamics and entirely put aside the uncertainty. In particular, since the missing values estimates have the risk of being imprecise, it motivates us to pay attention to reliable and less certain information differently. In this work, we propose a novel variational-recurrent imputation network (V-RIN), which unified imputation and prediction network, by taking into account the correlated features, temporal dynamics, and further utilizing the uncertainty to alleviate the risk of biased missing values estimates. Specifically, we leverage the deep generative model to estimate the missing values based on the distribution among variables and a recurrent imputation network to exploit the temporal relations in conjunction with utilization of the uncertainty. We validated the effectiveness of our proposed model with publicly available real-world EHR dataset, PhysioNet Challenge 2012, and compared the results with other state-of-the-art competing methods in the literature.
|
Our variational-recurrent imputation network (V-RIN) takes into account the correlated features, temporal dynamics, and further utilizes the uncertainty to alleviate the risk of biased missing values estimates.
| 5
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Q: As the basic building block of Convolutional Neural Networks (CNNs), the convolutional layer is designed to extract local patterns and lacks the ability to model global context in its nature. Many efforts have been recently made to complement CNNs with the global modeling ability, especially by a family of works on global feature interaction. In these works, the global context information is incorporated into local features before they are fed into convolutional layers. However, research on neuroscience reveals that, besides influences changing the inputs to our neurons, the neurons' ability of modifying their functions dynamically according to context is essential for perceptual tasks, which has been overlooked in most of CNNs. Motivated by this, we propose one novel Context-Gated Convolution (CGC) to explicitly modify the weights of convolutional layers adaptively under the guidance of global context. As such, being aware of the global context, the modulated convolution kernel of our proposed CGC can better extract representative local patterns and compose discriminative features. Moreover, our proposed CGC is lightweight, amenable to modern CNN architectures, and consistently improves the performance of CNNs according to extensive experiments on image classification, action recognition, and machine translation.
A:
|
A novel Context-Gated Convolution which incorporates global context information into CNNs by explicitly modulating convolution kernels, and thus captures more representative local patterns and extract discriminative features.
| 4
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Given the task definition and input, reply with output. In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Common-sense physical reasoning is an essential ingredient for any intelligent agent operating in the real-world. For example, it can be used to simulate the environment, or to infer the state of parts of the world that are currently unobserved. In order to match real-world conditions this causal knowledge must be learned without access to supervised data. To address this problem we present a novel method that learns to discover objects and model their physical interactions from raw visual images in a purely unsupervised fashion. It incorporates prior knowledge about the compositional nature of human perception to factor interactions between object-pairs and learn efficiently. On videos of bouncing balls we show the superior modelling capabilities of our method compared to other unsupervised neural approaches that do not incorporate such prior knowledge. We demonstrate its ability to handle occlusion and show that it can extrapolate learned knowledge to scenes with different numbers of objects.
|
We introduce a novel approach to common-sense physical reasoning that learns to discover objects and model their physical interactions from raw visual images in a purely unsupervised fashion
| 5
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Problem:Graph Neural Networks as a combination of Graph Signal Processing and Deep Convolutional Networks shows great power in pattern recognition in non-Euclidean domains. In this paper, we propose a new method to deploy two pipelines based on the duality of a graph to improve accuracy. By exploring the primal graph and its dual graph where nodes and edges can be treated as one another, we have exploited the benefits of both vertex features and edge features. As a result, we have arrived at a framework that has great potential in both semisupervised and unsupervised learning.
Solution:
|
A primal dual graph neural network model for semi-supervised learning
| 8
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Q: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
We present Predicted Variables, an approach to making machine learning (ML) a first class citizen in programming languages.
There is a growing divide in approaches to building systems: using human experts (e.g. programming) on the one hand, and using behavior learned from data (e.g. ML) on the other hand. PVars aim to make using ML in programming easier by hybridizing the two. We leverage the existing concept of variables and create a new type, a predicted variable. PVars are akin to native variables with one important distinction: PVars determine their value using ML when evaluated. We describe PVars and their interface, how they can be used in programming, and demonstrate the feasibility of our approach on three algorithmic problems: binary search, QuickSort, and caches.
We show experimentally that PVars are able to improve over the commonly used heuristics and lead to a better performance than the original algorithms.
As opposed to previous work applying ML to algorithmic problems, PVars have the advantage that they can be used within the existing frameworks and do not require the existing domain knowledge to be replaced. PVars allow for a seamless integration of ML into existing systems and algorithms.
Our PVars implementation currently relies on standard Reinforcement Learning (RL) methods. To learn faster, PVars use the heuristic function, which they are replacing, as an initial function. We show that PVars quickly pick up the behavior of the initial function and then improve performance beyond that without ever performing substantially worse -- allowing for a safe deployment in critical applications.
A:
|
We present Predicted Variables, an approach to making machine learning a first class citizen in programming languages.
| 7
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Deep reinforcement learning algorithms can learn complex behavioral skills, but real-world application of these methods requires a considerable amount of experience to be collected by the agent. In practical settings, such as robotics, this involves repeatedly attempting a task, resetting the environment between each attempt. However, not all tasks are easily or automatically reversible. In practice, this learning process requires considerable human intervention. In this work, we propose an autonomous method for safe and efficient reinforcement learning that simultaneously learns a forward and backward policy, with the backward policy resetting the environment for a subsequent attempt. By learning a value function for the backward policy, we can automatically determine when the forward policy is about to enter a non-reversible state, providing for uncertainty-aware safety aborts. Our experiments illustrate that proper use of the backward policy can greatly reduce the number of manual resets required to learn a task and can reduce the number of unsafe actions that lead to non-reversible states.
|
We propose an autonomous method for safe and efficient reinforcement learning that simultaneously learns a forward and backward policy, with the backward policy resetting the environment for a subsequent attempt.
| 0
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
The maximum mean discrepancy (MMD) between two probability measures P
and Q is a metric that is zero if and only if all moments of the two measures
are equal, making it an appealing statistic for two-sample tests. Given i.i.d. samples
from P and Q, Gretton et al. (2012) show that we can construct an unbiased
estimator for the square of the MMD between the two distributions. If P is a
distribution of interest and Q is the distribution implied by a generative neural
network with stochastic inputs, we can use this estimator to train our neural network.
However, in practice we do not always have i.i.d. samples from our target
of interest. Data sets often exhibit biases—for example, under-representation of
certain demographics—and if we ignore this fact our machine learning algorithms
will propagate these biases. Alternatively, it may be useful to assume our data has
been gathered via a biased sample selection mechanism in order to manipulate
properties of the estimating distribution Q.
In this paper, we construct an estimator for the MMD between P and Q when we
only have access to P via some biased sample selection mechanism, and suggest
methods for estimating this sample selection mechanism when it is not already
known. We show that this estimator can be used to train generative neural networks
on a biased data sample, to give a simulator that reverses the effect of that
bias.
|
We propose an estimator for the maximum mean discrepancy, appropriate when a target distribution is only accessible via a biased sample selection procedure, and show that it can be used in a generative network to correct for this bias.
| 0
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
In this paper we use the geometric properties of the optimal transport (OT) problem and the Wasserstein distances to define a prior distribution for the latent space of an auto-encoder. We introduce Sliced-Wasserstein Auto-Encoders (SWAE), that enable one to shape the distribution of the latent space into any samplable probability distribution without the need for training an adversarial network or having a likelihood function specified. In short, we regularize the auto-encoder loss with the sliced-Wasserstein distance between the distribution of the encoded training samples and a samplable prior distribution. We show that the proposed formulation has an efficient numerical solution that provides similar capabilities to Wasserstein Auto-Encoders (WAE) and Variational Auto-Encoders (VAE), while benefiting from an embarrassingly simple implementation. We provide extensive error analysis for our algorithm, and show its merits on three benchmark datasets.
|
In this paper we use the sliced-Wasserstein distance to shape the latent distribution of an auto-encoder into any samplable prior distribution.
| 0
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
You will be given a definition of a task first, then some input of the task.
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
We present an adversarial exploration strategy, a simple yet effective imitation learning scheme that incentivizes exploration of an environment without any extrinsic reward or human demonstration. Our framework consists of a deep reinforcement learning (DRL) agent and an inverse dynamics model contesting with each other. The former collects training samples for the latter, and its objective is to maximize the error of the latter. The latter is trained with samples collected by the former, and generates rewards for the former when it fails to predict the actual action taken by the former. In such a competitive setting, the DRL agent learns to generate samples that the inverse dynamics model fails to predict correctly, and the inverse dynamics model learns to adapt to the challenging samples. We further propose a reward structure that ensures the DRL agent collects only moderately hard samples and not overly hard ones that prevent the inverse model from imitating effectively. We evaluate the effectiveness of our method on several OpenAI gym robotic arm and hand manipulation tasks against a number of baseline models. Experimental results show that our method is comparable to that directly trained with expert demonstrations, and superior to the other baselines even without any human priors.
Output:
|
A simple yet effective imitation learning scheme that incentivizes exploration of an environment without any extrinsic reward or human demonstration.
| 1
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Teacher:In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Teacher: Now, understand the problem? Solve this instance: Recent evidence shows that convolutional neural networks (CNNs) are biased towards textures so that CNNs are non-robust to adversarial perturbations over textures, while traditional robust visual features like SIFT (scale-invariant feature transforms) are designed to be robust across a substantial range of affine distortion, addition of noise, etc with the mimic of human perception nature. This paper aims to leverage good properties of SIFT to renovate CNN architectures towards better accuracy and robustness. We borrow the scale-space extreme value idea from SIFT, and propose EVPNet (extreme value preserving network) which contains three novel components to model the extreme values: (1) parametric differences of Gaussian (DoG) to extract extrema, (2) truncated ReLU to suppress non-stable extrema and (3) projected normalization layer (PNL) to mimic PCA-SIFT like feature normalization. Experiments demonstrate that EVPNets can achieve similar or better accuracy than conventional CNNs, while achieving much better robustness on a set of adversarial attacks (FGSM,PGD,etc) even without adversarial training.
Student:
|
This paper aims to leverage good properties of robust visual features like SIFT to renovate CNN architectures towards better accuracy and robustness.
| 6
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
You will be given a definition of a task first, then some input of the task.
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Action segmentation as a milestone towards building automatic systems to understand untrimmed videos has received considerable attention in the recent years. It is typically being modeled as a sequence labeling problem but contains intrinsic and sufficient differences than text parsing or speech processing. In this paper, we introduce a novel hybrid temporal convolutional and recurrent network (TricorNet), which has an encoder-decoder architecture: the encoder consists of a hierarchy of temporal convolutional kernels that capture the local motion changes of different actions; the decoder is a hierarchy of recurrent neural networks that are able to learn and memorize long-term action dependencies after the encoding stage. Our model is simple but extremely effective in terms of video sequence labeling. The experimental results on three public action segmentation datasets have shown that the proposed model achieves superior performance over the state of the art.
Output:
|
We propose a new hybrid temporal network that achieves state-of-the-art performance on video action segmentation on three public datasets.
| 1
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Q: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
We introduce a new and rigorously-formulated PAC-Bayes few-shot meta-learning algorithm that implicitly learns a model prior distribution of interest. Our proposed method extends the PAC-Bayes framework from a single task setting to the few-shot meta-learning setting to upper-bound generalisation errors on unseen tasks. We also propose a generative-based approach to model the shared prior and task-specific posterior more expressively compared to the usual diagonal Gaussian assumption. We show that the models trained with our proposed meta-learning algorithm are well calibrated and accurate, with state-of-the-art calibration and classification results on mini-ImageNet benchmark, and competitive results in a multi-modal task-distribution regression.
A:
|
Bayesian meta-learning using PAC-Bayes framework and implicit prior distributions
| 7
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Teacher:In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Teacher: Now, understand the problem? Solve this instance: Su-Boyd-Candes (2014) made a connection between Nesterov's method and an ordinary differential equation (ODE). We show if a Hessian damping term is added to the ODE from Su-Boyd-Candes (2014), then Nesterov's method arises as a straightforward discretization of the modified ODE. Analogously, in the strongly convex case, a Hessian damping term is added to Polyak's ODE, which is then discretized to yield Nesterov's method for strongly convex functions. Despite the Hessian term, both second order ODEs can be represented as first order systems.
Established Liapunov analysis is used to recover the accelerated rates of convergence in both continuous and discrete time. Moreover, the Liapunov analysis can be extended to the case of stochastic gradients which allows the full gradient case to be considered as a special case of the stochastic case. The result is a unified approach to convex acceleration in both continuous and discrete time and in both the stochastic and full gradient cases.
Student:
|
We derive Nesterov's method arises as a straightforward discretization of an ODE different from the one in Su-Boyd-Candes and prove acceleration the stochastic case
| 6
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Teacher:In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Teacher: Now, understand the problem? Solve this instance: Inspired by the success of generative adversarial networks (GANs) in image domains, we introduce a novel hierarchical architecture for learning characteristic topological features from a single arbitrary input graph via GANs. The hierarchical architecture consisting of multiple GANs preserves both local and global topological features, and automatically partitions the input graph into representative stages for feature learning. The stages facilitate reconstruction and can be used as indicators of the importance of the associated topological structures. Experiments show that our method produces subgraphs retaining a wide range of topological features, even in early reconstruction stages. This paper contains original research on combining the use of GANs and graph topological analysis.
Student:
|
A GAN based method to learn important topological features of an arbitrary input graph.
| 6
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Input: Sequence prediction models can be learned from example sequences with a variety of training algorithms. Maximum likelihood learning is simple and efficient, yet can suffer from compounding error at test time.
Reinforcement learning such as policy gradient addresses the issue but can have prohibitively poor exploration efficiency. A rich set of other algorithms, such as data noising, RAML, and softmax policy gradient, have also been developed from different perspectives.
In this paper, we present a formalism of entropy regularized policy optimization, and show that the apparently distinct algorithms, including MLE, can be reformulated as special instances of the formulation. The difference between them is characterized by the reward function and two weight hyperparameters.
The unifying interpretation enables us to systematically compare the algorithms side-by-side, and gain new insights into the trade-offs of the algorithm design.
The new perspective also leads to an improved approach that dynamically interpolates among the family of algorithms, and learns the model in a scheduled way. Experiments on machine translation, text summarization, and game imitation learning demonstrate superiority of the proposed approach.
Output:
|
An entropy regularized policy optimization formalism subsumes a set of sequence prediction learning algorithms. A new interpolation algorithm with improved results on text generation and game imitation learning.
| 3
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
You will be given a definition of a task first, then some input of the task.
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
The challenge of learning disentangled representation has recently attracted much attention and boils down to a competition. Various methods based on variational auto-encoder have been proposed to solve this problem, by enforcing the independence between the representation and modifying the regularization term in the variational lower bound. However recent work by Locatello et al. (2018) has demonstrated that the proposed methods are heavily influenced by randomness and the choice of the hyper-parameter. This work is built upon the same framework in Stage 1 (Li et al., 2019), but with different settings; to make it self-contained, we provide this manuscript, which is unavoidably very similar to the report for Stage 1. In detail, in this work, instead of designing a new regularization term, we adopt the FactorVAE but improve the reconstruction performance and increase the capacity of network and the training step. The strategy turns out to be very effective in achieving disentanglement.
Output:
|
disentangled representation learning
| 1
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Given the task definition and input, reply with output. In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Model-based reinforcement learning (MBRL) aims to learn a dynamic model to reduce the number of interactions with real-world environments. However, due to estimation error, rollouts in the learned model, especially those of long horizon, fail to match the ones in real-world environments. This mismatching has seriously impacted the sample complexity of MBRL. The phenomenon can be attributed to the fact that previous works employ supervised learning to learn the one-step transition models, which has inherent difficulty ensuring the matching of distributions from multi-step rollouts. Based on the claim, we propose to learn the synthesized model by matching the distributions of multi-step rollouts sampled from the synthesized model and the real ones via WGAN. We theoretically show that matching the two can minimize the difference of cumulative rewards between the real transition and the learned one. Our experiments also show that the proposed model imitation method outperforms the state-of-the-art in terms of sample complexity and average return.
|
Our method incorporates WGAN to achieve occupancy measure matching for transition learning.
| 5
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Input: Most deep learning for NLP represents each word with a single point or single-mode region in semantic space, while the existing multi-mode word embeddings cannot represent longer word sequences like phrases or sentences. We introduce a phrase representation (also applicable to sentences) where each phrase has a distinct set of multi-mode codebook embeddings to capture different semantic facets of the phrase's meaning. The codebook embeddings can be viewed as the cluster centers which summarize the distribution of possibly co-occurring words in a pre-trained word embedding space. We propose an end-to-end trainable neural model that directly predicts the set of cluster centers from the input text sequence (e.g., a phrase or a sentence) during test time. We find that the per-phrase/sentence codebook embeddings not only provide a more interpretable semantic representation but also outperform strong baselines (by a large margin in some tasks) on benchmark datasets for unsupervised phrase similarity, sentence similarity, hypernym detection, and extractive summarization.
Output:
|
We propose an unsupervised way to learn multiple embeddings for sentences and phrases
| 3
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Given the task definition and input, reply with output. In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Recent research about margin theory has proved that maximizing the minimum margin like support vector machines does not necessarily lead to better performance, and instead, it is crucial to optimize the margin distribution. In the meantime, margin theory has been used to explain the empirical success of deep network in recent studies. In this paper, we present ODN (the Optimal margin Distribution Network), a network which embeds a loss function in regard to the optimal margin distribution. We give a theoretical analysis for our method using the PAC-Bayesian framework, which confirms the significance of the margin distribution for classification within the framework of deep networks. In addition, empirical results show that the ODN model always outperforms the baseline cross-entropy loss model consistently across different regularization situations. And our ODN
model also outperforms the cross-entropy loss (Xent), hinge loss and soft hinge loss model in generalization task through limited training data.
|
This paper presents a deep neural network embedding a loss function in regard to the optimal margin distribution, which alleviates the overfitting problem theoretically and empirically.
| 5
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Definition: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Input: Due to the success of deep learning to solving a variety of challenging machine learning tasks, there is a rising interest in understanding loss functions for training neural networks from a theoretical aspect. Particularly, the properties of critical points and the landscape around them are of importance to determine the convergence performance of optimization algorithms. In this paper, we provide a necessary and sufficient characterization of the analytical forms for the critical points (as well as global minimizers) of the square loss functions for linear neural networks. We show that the analytical forms of the critical points characterize the values of the corresponding loss functions as well as the necessary and sufficient conditions to achieve global minimum. Furthermore, we exploit the analytical forms of the critical points to characterize the landscape properties for the loss functions of linear neural networks and shallow ReLU networks. One particular conclusion is that: While the loss function of linear networks has no spurious local minimum, the loss function of one-hidden-layer nonlinear networks with ReLU activation function does have local minimum that is not global minimum.
Output:
|
We provide necessary and sufficient analytical forms for the critical points of the square loss functions for various neural networks, and exploit the analytical forms to characterize the landscape properties for the loss functions of these neural networks.
| 2
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Q: While it has not yet been proven, empirical evidence suggests that model generalization is related to local properties of the optima which can be described via the Hessian. We connect model generalization with the local property of a solution under the PAC-Bayes paradigm. In particular, we prove that model generalization ability is related to the Hessian, the higher-order "smoothness" terms characterized by the Lipschitz constant of the Hessian, and the scales of the parameters. Guided by the proof, we propose a metric to score the generalization capability of the model, as well as an algorithm that optimizes the perturbed model accordingly.
A:
|
a theory connecting Hessian of the solution and the generalization power of the model
| 4
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
|
Q: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Recent work has exhibited the surprising cross-lingual abilities of multilingual BERT (M-BERT) -- surprising since it is trained without any cross-lingual objective and with no aligned data. In this work, we provide a comprehensive study of the contribution of different components in M-BERT to its cross-lingual ability. We study the impact of linguistic properties of the languages, the architecture of the model, and of the learning objectives. The experimental study is done in the context of three typologically different languages -- Spanish, Hindi, and Russian -- and using two conceptually different NLP tasks, textual entailment and named entity recognition. Among our key conclusions is the fact that lexical overlap between languages plays a negligible role in the cross-lingual success, while the depth of the network is an important part of it
A:
|
Cross-Lingual Ability of Multilingual BERT: An Empirical Study
| 7
|
NIv2
|
task668_extreme_abstract_summarization
|
zs_opt
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In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Q: Modern deep neural networks can achieve high accuracy when the training distribution and test distribution are identically distributed, but this assumption is frequently violated in practice. When the train and test distributions are mismatched, accuracy can plummet. Currently there are few techniques that improve robustness to unforeseen data shifts encountered during deployment. In this work, we propose a technique to improve the robustness and uncertainty estimates of image classifiers. We propose AugMix, a data processing technique that is simple to implement, adds limited computational overhead, and helps models withstand unforeseen corruptions. AugMix significantly improves robustness and uncertainty measures on challenging image classification benchmarks, closing the gap between previous methods and the best possible performance in some cases by more than half.
A:
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We obtain state-of-the-art on robustness to data shifts, and we maintain calibration under data shift even though even when accuracy drops
| 4
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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Given the task definition and input, reply with output. In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
We present Random Partition Relaxation (RPR), a method for strong quantization of the parameters of convolutional neural networks to binary (+1/-1) and ternary (+1/0/-1) values. Starting from a pretrained model, we first quantize the weights and then relax random partitions of them to their continuous values for retraining before quantizing them again and switching to another weight partition for further adaptation. We empirically evaluate the performance of RPR with ResNet-18, ResNet-50 and GoogLeNet on the ImageNet classification task for binary and ternary weight networks. We show accuracies beyond the state-of-the-art for binary- and ternary-weight GoogLeNet and competitive performance for ResNet-18 and ResNet-50 using a SGD-based training method that can easily be integrated into existing frameworks.
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State-of-the-art training method for binary and ternary weight networks based on alternating optimization of randomly relaxed weight partitions
| 5
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NIv2
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task668_extreme_abstract_summarization
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zs_opt
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