Summary of Grad Queue : a Probabilistic Framework to Reinforce Sparse Gradients, by Irfan Mohammad Al Hasib
Grad Queue : A probabilistic framework to reinforce sparse gradients
by Irfan Mohammad Al Hasib
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 robust mechanism to reinforce sparse components within random batches of data points during large batch updates. The method uses a finite queue of online gradients to determine expected instantaneous statistics, measuring scarcity using these statistics. To minimize conflicting components in large mini-batches, samples are grouped with aligned objectives by clustering based on inherent feature space. Sparsity is measured for each centroid and weighted accordingly. The paper’s contribution is to restore intra-mini-batch diversity while widening the optimal batch boundary, driving it deeper towards the minima. The method outperforms mini-batch gradient descent on CIFAR10, MNIST, and Reuters News category dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn better by fixing a problem called “lost gradients.” When we update lots of information at once, important details can get lost. To solve this, the authors propose a new way to use small groups of data points that are similar and aligned with each other’s goals. This method helps make sure that rare or important information isn’t ignored by the machine learning process. The authors tested their approach on several datasets and found it worked better than a traditional method. |
Keywords
» Artificial intelligence » Clustering » Gradient descent » Machine learning
