Summary of Quantile Activation: Correcting a Failure Mode Of Ml Models, by Aditya Challa et al.
Quantile Activation: Correcting a Failure Mode of ML Models
by Aditya Challa, Sravan Danda, Laurent Najman, Snehanshu Saha
First submitted to arxiv on: 19 May 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 This paper addresses a fundamental limitation in machine learning models, specifically when features have equal likelihood of belonging to two classes (0 and 1). Existing approaches struggle to classify samples under such conditions. However, the authors identify a solvable case where probabilities vary with context distribution. They propose adapting standard neural network architectures, such as MLPs or CNNs, to handle this scenario. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models can get stuck when features are equally likely to belong to two classes. This makes it hard for them to tell which class something belongs to. The good news is that there’s a way around this problem if the likelihood of each class changes depending on the context. Right now, most neural networks aren’t designed to handle this situation. |
Keywords
» Artificial intelligence » Likelihood » Machine learning » Neural network
