Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Post-training Quantization for Neural Networks with Provable Guarantees   While neural networks have been remarkably successful in a wide array of
applications, implementing them in resource-constrained hardware remains an
area of intense research. By replacing the weights of a neural network with
quantized (e.g., 4-bit, or binary) counterparts, massive savings in computation
cost, memory, and power consumption are attained. We modify a post-training
neural-network quantization method, GPFQ, that is based on a greedy
path-following mechanism, and rigorously analyze its error. We prove that for
quantizing a single-layer network, the relative square error essentially decays
linearly in the number of weights -- i.e., level of over-parametrization. Our
result holds across a range of input distributions and for both fully-connected
and convolutional architectures. To empirically evaluate the method, we
quantize several common architectures with few bits per weight, and test them
on ImageNet, showing only minor loss of accuracy. We also demonstrate that
standard modifications, such as bias correction and mixed precision
quantization, further improve accuracy.

23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin