Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks

TL;DR

This paper introduces a novel approach using concave regularizers to identify sparse subnetworks in neural networks, enhancing training efficiency and model sparsity beyond existing methods.

Contribution

It proposes a new class of lottery ticket algorithms leveraging concave regularization to better find effective sparse subnetworks in neural networks.

Findings

Improves performance over state-of-the-art algorithms

Theoretically effective in convex frameworks

Demonstrates success across various datasets and architectures

Abstract

The design of sparse neural networks, i.e., of networks with a reduced number of parameters, has been attracting increasing research attention in the last few years. The use of sparse models may significantly reduce the computational and storage footprint in the inference phase. In this context, the lottery ticket hypothesis (LTH) constitutes a breakthrough result, that addresses not only the performance of the inference phase, but also of the training phase. It states that it is possible to extract effective sparse subnetworks, called winning tickets, that can be trained in isolation. The development of effective methods to play the lottery, i.e., to find winning tickets, is still an open problem. In this article, we propose a novel class of methods to play the lottery. The key point is the use of concave regularization to promote the sparsity of a relaxed binary mask, which represents the network topology. We theoretically analyze the effectiveness of the proposed method in the convex framework. Then, we propose extended numerical tests on various datasets and architectures, that show that the proposed method can improve the performance of state-of-the-art algorithms.