Authors: Juncai He, Liangchen Liu, Yen-Hsi, Tsai

Published on: February 05, 2024

Impact Score: 8.38

Arxiv code: Arxiv:2402.03021

Summary

• What is new: Introduces a novel gradient descent approach inspired by multiscale algorithms for enhancing training efficiency.
• Why this is important: Difficulty in training machine learning models efficiently due to large variations in scale in the dataset.
• What the research proposes: A new gradient descent method that adapts to multiscale structures in data for improved learning rate selection.
• Results: Demonstrated higher training efficiency, especially in later stages of model training.

Technical Details

Technological frameworks used: Deep learning

Models used: Multiscale algorithms

Data used: Datasets with large variations in scale

Potential Impact

Companies in sectors like AI development, data science services, and industries relying on large-scale machine learning models.

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