Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scalable Neural Network Compression and Pruning Using Hard Clustering
  and L1 Regularization   We propose a simple and easy to implement neural network compression
algorithm that achieves results competitive with more complicated
state-of-the-art methods. The key idea is to modify the original optimization
problem by adding K independent Gaussian priors (corresponding to the k-means
objective) over the network parameters to achieve parameter quantization, as
well as an L1 penalty to achieve pruning. Unlike many existing
quantization-based methods, our method uses hard clustering assignments of
network parameters, which adds minimal change or overhead to standard network
training. We also demonstrate experimentally that tying neural network
parameters provides less gain in generalization performance than changing
network architecture and connectivity patterns entirely.

18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th