Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Neural Network Training via Forward and Backward Propagation
  Sparsification   Sparse training is a natural idea to accelerate the training speed of deep
neural networks and save the memory usage, especially since large modern neural
networks are significantly over-parameterized. However, most of the existing
methods cannot achieve this goal in practice because the chain rule based
gradient (w.r.t. structure parameters) estimators adopted by previous methods
require dense computation at least in the backward propagation step. This paper
solves this problem by proposing an efficient sparse training method with
completely sparse forward and backward passes. We first formulate the training
process as a continuous minimization problem under global sparsity constraint.
We then separate the optimization process into two steps, corresponding to
weight update and structure parameter update. For the former step, we use the
conventional chain rule, which can be sparse via exploiting the sparse
structure. For the latter step, instead of using the chain rule based gradient
estimators as in existing methods, we propose a variance reduced policy
gradient estimator, which only requires two forward passes without backward
propagation, thus achieving completely sparse training. We prove that the
variance of our gradient estimator is bounded. Extensive experimental results
on real-world datasets demonstrate that compared to previous methods, our
algorithm is much more effective in accelerating the training process, up to an
order of magnitude faster.

33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci