How Does Momentum Benefit Deep Neural Networks Architecture Design? A Few Case Studies

TL;DR

This paper reviews how incorporating momentum into neural network architectures like RNNs, neural ODEs, and transformers can address training challenges, improve efficiency, and enhance performance through theoretical insights and case studies.

Contribution

It provides a theoretical and empirical framework demonstrating the benefits of momentum in neural architecture design across various models.

Findings

Momentum overcomes vanishing gradients in RNNs and neural ODEs.

Momentum reduces stiffness in neural ODEs, improving computational efficiency.

Momentum enhances the efficiency and accuracy of transformers.

Abstract

We present and review an algorithmic and theoretical framework for improving neural network architecture design via momentum. As case studies, we consider how momentum can improve the architecture design for recurrent neural networks (RNNs), neural ordinary differential equations (ODEs), and transformers. We show that integrating momentum into neural network architectures has several remarkable theoretical and empirical benefits, including 1) integrating momentum into RNNs and neural ODEs can overcome the vanishing gradient issues in training RNNs and neural ODEs, resulting in effective learning long-term dependencies. 2) momentum in neural ODEs can reduce the stiffness of the ODE dynamics, which significantly enhances the computational efficiency in training and testing. 3) momentum can improve the efficiency and accuracy of transformers.