Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: State-Reification Networks: Improving Generalization by Modeling the
  Distribution of Hidden Representations   Machine learning promises methods that generalize well from finite labeled
data. However, the brittleness of existing neural net approaches is revealed by
notable failures, such as the existence of adversarial examples that are
misclassified despite being nearly identical to a training example, or the
inability of recurrent sequence-processing nets to stay on track without
teacher forcing. We introduce a method, which we refer to as \emph{state
reification}, that involves modeling the distribution of hidden states over the
training data and then projecting hidden states observed during testing toward
this distribution. Our intuition is that if the network can remain in a
familiar manifold of hidden space, subsequent layers of the net should be well
trained to respond appropriately. We show that this state-reification method
helps neural nets to generalize better, especially when labeled data are
sparse, and also helps overcome the challenge of achieving robust
generalization with adversarial training.

27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
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The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio