Summary of Towards Interpretable Deep Local Learning with Successive Gradient Reconciliation, by Yibo Yang et al.
Towards Interpretable Deep Local Learning with Successive Gradient Reconciliation
by Yibo Yang, Xiaojie Li, Motasem Alfarra, Hasan Hammoud, Adel Bibi, Philip Torr, Bernard Ghanem
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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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 paper proposes a novel local training strategy for neural networks that reconciles gradients between neighboring modules without breaking gradient isolation or introducing learnable parameters. The method builds upon the concept of non-greedy layer-wise training and shows promise in achieving competitive performance with global back-propagation (BP) while reducing memory consumption by over 40%. The authors theoretically investigate the convergence of local errors and propose a strategy that regularizes gradient reconciliation between modules, making it suitable for integration into both local-BP and BP-free settings. In experiments, the method demonstrates significant performance improvements compared to previous methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding new ways to train neural networks without using a traditional technique called back-propagation (BP). Right now, BP uses a lot of memory and doesn’t match how our brains work, so scientists are trying to find better alternatives. One approach, local learning, has shown promise but hasn’t been fully explored. This paper studies what happens when you try to combine the errors from different parts of the network without using BP. The authors then propose a new way to train neural networks that works well and uses less memory than before. They tested it on big datasets and found that it performs just as well as BP, but with a much smaller memory footprint. |
