Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Long Distance Relationships without Time Travel: Boosting the
  Performance of a Sparse Predictive Autoencoder in Sequence Modeling   In sequence learning tasks such as language modelling, Recurrent Neural
Networks must learn relationships between input features separated by time.
State of the art models such as LSTM and Transformer are trained by
backpropagation of losses into prior hidden states and inputs held in memory.
This allows gradients to flow from present to past and effectively learn with
perfect hindsight, but at a significant memory cost. In this paper we show that
it is possible to train high performance recurrent networks using information
that is local in time, and thereby achieve a significantly reduced memory
footprint. We describe a predictive autoencoder called bRSM featuring recurrent
connections, sparse activations, and a boosting rule for improved cell
utilization. The architecture demonstrates near optimal performance on a
non-deterministic (stochastic) partially-observable sequence learning task
consisting of high-Markov-order sequences of MNIST digits. We find that this
model learns these sequences faster and more completely than an LSTM, and offer
several possible explanations why the LSTM architecture might struggle with the
partially observable sequence structure in this task. We also apply our model
to a next word prediction task on the Penn Treebank (PTB) dataset. We show that
a 'flattened' RSM network, when paired with a modern semantic word embedding
and the addition of boosting, achieves 103.5 PPL (a 20-point improvement over
the best N-gram models), beating ordinary RNNs trained with BPTT and
approaching the scores of early LSTM implementations. This work provides
encouraging evidence that strong results on challenging tasks such as language
modelling may be possible using less memory intensive, biologically-plausible
training regimes.

21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci