Summary of How Good Is a Single Basin?, by Kai Lion et al.
How Good is a Single Basin?
by Kai Lion, Lorenzo Noci, Thomas Hofmann, Gregor Bachmann
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Medium Difficulty summary: This paper investigates the impact of connectivity on deep ensembles, a popular technique in neural networks. The authors construct “connected” ensembles that share knowledge across different loss landscapes, which are often seen as the key to deep ensemble success. Surprisingly, they find that increased connectivity actually hurts performance. However, by incorporating knowledge from other basins using distillation, they show that the gap in performance can be mitigated. The results suggest that while extra-basin knowledge is present in any given basin, it cannot be easily harnessed without learning it from other basins. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper looks at how different neural networks work together to get better results. It’s like having a team of experts working on the same problem. The authors found that when these teams share their knowledge with each other, it doesn’t always make them better. But if they learn from each other in a special way, called distillation, then they can actually perform better than before. This means that even though there’s valuable information out there, you need to figure out how to use it correctly to get the best results. |
Keywords
* Artificial intelligence * Distillation
