Summary of Beyond Adaptive Gradient: Fast-controlled Minibatch Algorithm For Large-scale Optimization, by Corrado Coppola et al.
Beyond adaptive gradient: Fast-Controlled Minibatch Algorithm for large-scale optimization
by Corrado Coppola, Lorenzo Papa, Irene Amerini, Laura Palagi
First submitted to arxiv on: 24 Nov 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 proposes F-CMA, a novel adaptive gradient method that addresses limitations of existing methods. The F-CMA algorithm uses a random reshuffling method, sufficient decrease condition, and line-search procedure to ensure loss reduction per epoch. The authors prove the global convergence of F-CMA to a stationary point using deterministic methods. They integrate F-CMA into conventional training protocols for classification tasks involving convolutional neural networks (CNNs) and vision transformer models, allowing for direct comparison with popular optimizers. Computational tests show significant improvements in overall training time, per-epoch efficiency, and model accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn better by creating a new way to make them adapt quickly. The old methods were slow and needed lots of memory, but this new one is faster and uses less memory. The authors also prove that it will always get the right answer. They tested it on different types of models and saw big improvements in how fast it learned and how well it did. |
Keywords
» Artificial intelligence » Classification » Vision transformer
