Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks
This addresses the challenge of reducing computational and storage costs in neural networks for researchers and practitioners, but it is incremental as it builds on the lottery ticket hypothesis.
The paper tackles the problem of finding sparse subnetworks (winning tickets) in neural networks by proposing a novel method using concave regularization on a relaxed binary mask, and demonstrates improved performance over state-of-the-art algorithms in numerical tests on various datasets and architectures.
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.