Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Embedding Differentiable Sparsity into Deep Neural Network   In this paper, we propose embedding sparsity into the structure of deep
neural networks, where model parameters can be exactly zero during training
with the stochastic gradient descent. Thus, it can learn the sparsified
structure and the weights of networks simultaneously. The proposed approach can
learn structured as well as unstructured sparsity.

11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin