Summary of Geometrical Structures Of Digital Fluctuations in Parameter Space Of Neural Networks Trained with Adaptive Momentum Optimization, by Igor V. Netay
Geometrical structures of digital fluctuations in parameter space of neural networks trained with adaptive momentum optimization
by Igor V. Netay
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA)
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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 The proposed paper explores the numerical instability of neural networks optimized using adaptive momentum during long-run training. The authors demonstrate that this issue affects not only large-scale models but also shallow narrow networks, leading to divergence. To support their theory, they conducted experiments on over 1,600 neural networks trained for 50,000 epochs and observed the same behavior in both stable and unstable training segments. This phenomenon is attributed to the formation of double-twisted spirals in the parameter space, caused by alternating numerical perturbations with relaxation oscillations in first- and second-momentum values. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how neural networks work when using a special way to update their weights called adaptive momentum. The authors found that this method can make the network’s weights go crazy after a while, even if it starts out working well. They did lots of experiments with over 1,600 different networks and saw that this problem happens even in smaller networks, not just big ones. This is important because it means we need to find new ways to keep our neural networks stable and working correctly. |
