Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Proximal Mean-field for Neural Network Quantization   Compressing large Neural Networks (NN) by quantizing the parameters, while
maintaining the performance is highly desirable due to reduced memory and time
complexity. In this work, we cast NN quantization as a discrete labelling
problem, and by examining relaxations, we design an efficient iterative
optimization procedure that involves stochastic gradient descent followed by a
projection. We prove that our simple projected gradient descent approach is, in
fact, equivalent to a proximal version of the well-known mean-field method.
These findings would allow the decades-old and theoretically grounded research
on MRF optimization to be used to design better network quantization schemes.
Our experiments on standard classification datasets (MNIST, CIFAR10/100,
TinyImageNet) with convolutional and residual architectures show that our
algorithm obtains fully-quantized networks with accuracies very close to the
floating-point reference networks.

17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph