Tuning Learning Rates with the Cumulative-Learning Constant
By: Nathan Faraj
Potential Business Impact:
Makes computer learning faster and better.
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.
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