Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration

TL;DR

This paper introduces a new sparse deep learning framework that addresses local traps and miscalibration issues, providing theoretical foundations and algorithms with proven convergence and improved performance.

Contribution

It develops a theoretical basis for sparse deep neural networks and proposes prior annealing algorithms with guarantees of convergence to the global optimum.

Findings

The framework effectively mitigates local traps and miscalibration.

Proposed algorithms are asymptotically guaranteed to converge globally.

Numerical results show superior performance over existing methods.

Abstract

Deep learning has powered recent successes of artificial intelligence (AI). However, the deep neural network, as the basic model of deep learning, has suffered from issues such as local traps and miscalibration. In this paper, we provide a new framework for sparse deep learning, which has the above issues addressed in a coherent way. In particular, we lay down a theoretical foundation for sparse deep learning and propose prior annealing algorithms for learning sparse neural networks. The former has successfully tamed the sparse deep neural network into the framework of statistical modeling, enabling prediction uncertainty correctly quantified. The latter can be asymptotically guaranteed to converge to the global optimum, enabling the validity of the down-stream statistical inference. Numerical result indicates the superiority of the proposed method compared to the existing ones.