Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Memory Bounded Deep Convolutional Networks   In this work, we investigate the use of sparsity-inducing regularizers during
training of Convolution Neural Networks (CNNs). These regularizers encourage
that fewer connections in the convolution and fully connected layers take
non-zero values and in effect result in sparse connectivity between hidden
units in the deep network. This in turn reduces the memory and runtime cost
involved in deploying the learned CNNs. We show that training with such
regularization can still be performed using stochastic gradient descent
implying that it can be used easily in existing codebases. Experimental
evaluation of our approach on MNIST, CIFAR, and ImageNet datasets shows that
our regularizers can result in dramatic reductions in memory requirements. For
instance, when applied on AlexNet, our method can reduce the memory consumption
by a factor of four with minimal loss in accuracy.

16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph