Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Gradient Estimation Using Stochastic Computation Graphs   In a variety of problems originating in supervised, unsupervised, and
reinforcement learning, the loss function is defined by an expectation over a
collection of random variables, which might be part of a probabilistic model or
the external world. Estimating the gradient of this loss function, using
samples, lies at the core of gradient-based learning algorithms for these
problems. We introduce the formalism of stochastic computation
graphs---directed acyclic graphs that include both deterministic functions and
conditional probability distributions---and describe how to easily and
automatically derive an unbiased estimator of the loss function's gradient. The
resulting algorithm for computing the gradient estimator is a simple
modification of the standard backpropagation algorithm. The generic scheme we
propose unifies estimators derived in variety of prior work, along with
variance-reduction techniques therein. It could assist researchers in
developing intricate models involving a combination of stochastic and
deterministic operations, enabling, for example, attention, memory, and control
actions.

9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
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The categories of this paper are: adap-org
37
The categories of this paper are: supr-con