Summary of Variational Learning Is Effective For Large Deep Networks, by Yuesong Shen et al.
Variational Learning is Effective for Large Deep Networks
by Yuesong Shen, Nico Daheim, Bai Cong, Peter Nickl, Gian Maria Marconi, Clement Bazan, Rio Yokota, Iryna Gurevych, Daniel Cremers, Mohammad Emtiyaz Khan, Thomas Möllenhoff
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Optimization and Control (math.OC); Machine Learning (stat.ML)
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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 In this research paper, the authors provide empirical evidence against the common misconception that variational learning is ineffective for large neural networks. They demonstrate that Improved Variational Online Newton (IVON) consistently outperforms Adam in training GPT-2 and ResNets from scratch, with similar computational costs but improved predictive uncertainty. The authors also showcase IVON’s applications in finetuning and model merging in Large Language Models, predicting generalization error, and estimating sensitivity to data. Overall, the study shows that variational learning is indeed effective. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows that a new way of training neural networks called variational learning can be very good for large networks. The researchers found an optimizer called IVON that works well with this type of learning. They compared it to another popular optimizer called Adam and found that IVON does just as well or even better. This is important because it means we can use variational learning to train bigger and more complex neural networks, which could be useful for tasks like language processing. |
Keywords
* Artificial intelligence * Generalization * Gpt
