Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Drop-Bottleneck: Learning Discrete Compressed Representation for
  Noise-Robust Exploration   We propose a novel information bottleneck (IB) method named Drop-Bottleneck,
which discretely drops features that are irrelevant to the target variable.
Drop-Bottleneck not only enjoys a simple and tractable compression objective
but also additionally provides a deterministic compressed representation of the
input variable, which is useful for inference tasks that require consistent
representation. Moreover, it can jointly learn a feature extractor and select
features considering each feature dimension's relevance to the target task,
which is unattainable by most neural network-based IB methods. We propose an
exploration method based on Drop-Bottleneck for reinforcement learning tasks.
In a multitude of noisy and reward sparse maze navigation tasks in VizDoom
(Kempka et al., 2016) and DMLab (Beattie et al., 2016), our exploration method
achieves state-of-the-art performance. As a new IB framework, we demonstrate
that Drop-Bottleneck outperforms Variational Information Bottleneck (VIB)
(Alemi et al., 2017) in multiple aspects including adversarial robustness and
dimensionality reduction.

11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph