New research explores how Adam optimizer's memory can slow down progress

A new research paper introduces "Second-Moment Memory in Coordinatewise Adam," exploring the impact of memory in the Adam optimizer. The study demonstrates that the optimizer's memory of past squared gradients can hinder progress towards the optimal solution, even with finite-variance stochastic gradients. The paper derives a theoretical bound showing that longer second-moment memory can slow down optimization, particularly in smooth convex problems. AI

IMPACT This research could lead to more efficient training of machine learning models by refining optimization techniques.

RANK_REASON The cluster contains a research paper detailing theoretical findings about an optimization algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Adam
• alphaXiv
• arXiv
• CatalyzeX Code Finder for Papers
• CORE Recommender
• cs.LG
• DagsHub
• Gotit.pub
• Hugging Face
• IArxiv Recommender
• Influence Flower
• ScienceCast

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →