Summary of Fine-tuning with Very Large Dropout, by Jianyu Zhang et al.
Fine-tuning with Very Large Dropout
by Jianyu Zhang, Léon Bottou
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposed approach utilizes ensemble techniques to address scenarios where training and testing data follow different distributions. By incorporating richer representations that account for out-of-distribution performances, this method can effectively handle multiple data distributions. The use of stochastic gradient procedures with implicit sparsity biases is also explored, highlighting the importance of considering these factors in machine learning applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning has a problem: when training and testing data are different, it’s hard to get good results. Some researchers have found that by combining many models, they can do better than just one model. This new approach takes this idea further by creating more complex representations of the data that can handle different distributions. It also looks at how common training procedures can introduce biases and tries to fix these issues. |
Keywords
* Artificial intelligence * Machine learning
