Summary of Infinite Width Limits Of Self Supervised Neural Networks, by Maximilian Fleissner et al.
Infinite Width Limits of Self Supervised Neural Networks
by Maximilian Fleissner, Gautham Govind Anil, Debarghya Ghoshdastidar
First submitted to arxiv on: 17 Nov 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 The abstract discusses the connection between two concepts: the Neural Tangent Kernel (NTK) in deep learning and self-supervised learning. The NTK is a tool used to analyze supervised deep neural networks using kernel regression, while self-supervised learning involves training models on unlabeled data. Researchers have hypothesized that kernel models for self-supervised learning can provide insights into wide neural networks through the NTK. However, this connection has not been thoroughly explored or mathematically justified. The paper bridges this gap by analyzing two-layer neural networks trained with the Barlow Twins loss and proves that the NTK becomes constant as the network width approaches infinity. This work provides a mathematical foundation for using classic kernel theory to understand self-supervised learning of wide neural networks, which is crucial for developing effective algorithms in this area. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can use tools from one part of machine learning (deep neural networks) to help us understand another part (self-supervised learning). Researchers have been wondering if these two areas are connected through something called the Neural Tangent Kernel. The authors of this paper studied how a specific type of neural network, trained in a certain way, behaves when we look at it through the NTK lens. They found that as the network gets wider and wider, the NTK becomes constant, which means we can use classic kernel theory to understand self-supervised learning. This is important because it helps us develop better algorithms for this area of machine learning. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Neural network » Regression » Self supervised » Supervised
