Summary of Novel Gradient Sparsification Algorithm Via Bayesian Inference, by Ali Bereyhi and Ben Liang and Gary Boudreau and Ali Afana
Novel Gradient Sparsification Algorithm via Bayesian Inference
by Ali Bereyhi, Ben Liang, Gary Boudreau, Ali Afana
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); Signal Processing (eess.SP)
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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 regularized Top-k (RegTop-k) algorithm controls the learning rate scaling of error accumulation in distributed gradient descent. By treating gradient sparsification as an inference problem, RegTop-k determines a Bayesian optimal sparsification mask via maximum-a-posteriori estimation, utilizing past aggregated gradients to evaluate posterior statistics and prioritize local gradient entries. Numerical experiments with ResNet-18 on CIFAR-10 show that at 0.1% sparsification, RegTop-k achieves about 8% higher accuracy than standard Top-k. The algorithm addresses the issue of error accumulation in distributed gradient descent, which can deteriorate convergence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make calculations more efficient when training neural networks. It’s called regularized Top-k, and it helps prevent slow-downs in the learning process by controlling how much information is kept or discarded. The method works by looking at past calculations and deciding which ones are most important. This approach can lead to better results, such as 8% higher accuracy, compared to traditional methods. |
Keywords
» Artificial intelligence » Gradient descent » Inference » Mask » Resnet
