Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient
  Backpropagation Through Categorical Variables   To address the challenge of backpropagating the gradient through categorical
variables, we propose the augment-REINFORCE-swap-merge (ARSM) gradient
estimator that is unbiased and has low variance. ARSM first uses variable
augmentation, REINFORCE, and Rao-Blackwellization to re-express the gradient as
an expectation under the Dirichlet distribution, then uses variable swapping to
construct differently expressed but equivalent expectations, and finally shares
common random numbers between these expectations to achieve significant
variance reduction. Experimental results show ARSM closely resembles the
performance of the true gradient for optimization in univariate settings;
outperforms existing estimators by a large margin when applied to categorical
variational auto-encoders; and provides a "try-and-see self-critic" variance
reduction method for discrete-action policy gradient, which removes the need of
estimating baselines by generating a random number of pseudo actions and
estimating their action-value functions.

15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph