Tuning Learning Rates with the Cumulative-Learning Constant

TL;DR

This paper presents a new approach for optimizing learning rates by discovering a proportionality with dataset sizes and introducing a cumulative learning constant to improve training efficiency and performance.

Contribution

It uncovers a proportionality between learning rates and dataset sizes and introduces a cumulative learning constant for designing better learning rate schedules.

Findings

Discovered proportionality between learning rates and dataset sizes

Introduced the concept of a cumulative learning constant

Potential to enhance training efficiency and performance

Abstract

This paper introduces a novel method for optimizing learning rates in machine learning. A previously unrecognized proportionality between learning rates and dataset sizes is discovered, providing valuable insights into how dataset scale influences training dynamics. Additionally, a cumulative learning constant is identified, offering a framework for designing and optimizing advanced learning rate schedules. These findings have the potential to enhance training efficiency and performance across a wide range of machine learning applications.