Summary of A Comprehensive Framework For Analyzing the Convergence Of Adam: Bridging the Gap with Sgd, by Ruinan Jin et al.
A Comprehensive Framework for Analyzing the Convergence of Adam: Bridging the Gap with SGD
by Ruinan Jin, Xiao Li, Yaoliang Yu, Baoxiang Wang
First submitted to arxiv on: 6 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper focuses on improving our understanding of Adaptive Moment Estimation (Adam), a popular optimization algorithm in deep learning. Adam’s adaptive learning rates and efficiency make it well-suited for large-scale datasets, but theoretical convergence analysis has been limited by restrictive assumptions about gradient bounds. The authors aim to relax these constraints, enabling more general applications of Adam. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Adam is an important tool for training deep neural networks. It helps with big datasets by adjusting how much it learns from each example. But researchers haven’t fully understood why it works so well. They want to make sure it will work even when the data is tricky or noisy. This matters because it could help us use Adam on a wider range of problems. |
Keywords
* Artificial intelligence * Deep learning * Optimization
