Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data-Efficient Structured Pruning via Submodular Optimization   Structured pruning is an effective approach for compressing large pre-trained
neural networks without significantly affecting their performance, which
involves removing redundant regular regions of weights. However, current
structured pruning methods are highly empirical in nature, do not provide any
theoretical guarantees, and often require fine-tuning, which makes them
inapplicable in the limited-data regime. We propose a principled data-efficient
structured pruning method based on submodular optimization. In particular, for
a given layer, we select neurons/channels to prune and corresponding new
weights for the next layer, that minimize the change in the next layer's input
induced by pruning. We show that this selection problem is a weakly submodular
maximization problem, thus it can be provably approximated using an efficient
greedy algorithm. Our method is one of the few in the literature that uses only
a limited-number of training data and no labels. Our experimental results
demonstrate that our method outperforms popular baseline methods in various
one-shot pruning settings.

27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con