Summary of Navigating Extremes: Dynamic Sparsity in Large Output Spaces, by Nasib Ullah and Erik Schultheis and Mike Lasby and Yani Ioannou and Rohit Babbar
Navigating Extremes: Dynamic Sparsity in Large Output Spaces
by Nasib Ullah, Erik Schultheis, Mike Lasby, Yani Ioannou, Rohit Babbar
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 explores Dynamic Sparse Training (DST) as an alternative to post-training pruning for generating efficient models. Current implementations fail to leverage DST’s memory efficiency due to inefficient sparse matrix multiplication on GPUs, so the authors apply recent advances in semi-structured sparse training to classification tasks with large output spaces. The classification layer consumes significant memory, making DST crucial. However, switching from dense to fixed fan-in sparse layers updated with sparse evolutionary training (SET) hampers training convergence, especially at larger label spaces. Poor gradient flow from the sparse classifier to the dense text encoder hinders learning good input representations. By adding an intermediate layer or auxiliary objective, the authors recover most of the generalization performance of the dense model. DST demonstrates practical benefits in a challenging domain with millions of labels on commodity hardware. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making machines learn faster and use less memory. Right now, there’s a way to make models more efficient called Dynamic Sparse Training (DST). But current methods don’t really work because they’re not very good at doing math problems quickly enough. The authors found a way to make DST work better by using new ideas in machine learning. They tested it on big tasks with millions of possible answers and showed that it can be done on regular computers, which is important for real-world applications. |
Keywords
» Artificial intelligence » Classification » Encoder » Generalization » Machine learning » Pruning
