Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning deep representations by mutual information estimation and
  maximization   In this work, we perform unsupervised learning of representations by
maximizing mutual information between an input and the output of a deep neural
network encoder. Importantly, we show that structure matters: incorporating
knowledge about locality of the input to the objective can greatly influence a
representation's suitability for downstream tasks. We further control
characteristics of the representation by matching to a prior distribution
adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a
number of popular unsupervised learning methods and competes with
fully-supervised learning on several classification tasks. DIM opens new
avenues for unsupervised learning of representations and is an important step
towards flexible formulations of representation-learning objectives for
specific end-goals.

6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex