Summary of Agnostic Learning Of Arbitrary Relu Activation Under Gaussian Marginals, by Anxin Guo et al.
Agnostic Learning of Arbitrary ReLU Activation under Gaussian Marginals
by Anxin Guo, Aravindan Vijayaraghavan
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); 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 paper, researchers tackle a fundamental problem in machine learning: learning an arbitrarily-biased ReLU activation (or neuron) over Gaussian marginals with the squared loss objective. Despite being the basic building block of modern neural networks, little is understood about whether one arbitrary ReLU neuron is learnable in the non-realizable setting. The authors investigate this question and explore approximation guarantees for polynomial time algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to learn a biased neuron in a complex data set. Scientists want to know if they can teach a computer to understand and make decisions based on biased information, even when there’s no real-world example of the same situation. The research is important because it helps us understand how artificial intelligence works. |
Keywords
» Artificial intelligence » Machine learning » Relu
