Summary of The Effects Of Multi-task Learning on Relu Neural Network Functions, by Julia Nakhleh et al.
The Effects of Multi-Task Learning on ReLU Neural Network Functions
by Julia Nakhleh, Joseph Shenouda, Robert D. Nowak
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 paper explores the properties of solutions in multi-task shallow ReLU neural networks, where the goal is to fit a dataset with minimal sum of squared weights. Surprisingly, individual task solutions resemble those obtained through kernel regression, revealing a connection between neural networks and kernel methods. The study shows that single-task problems are equivalent to minimum norm interpolation problems in non-Hilbertian Banach spaces, whereas multi-task problems coincide with minimum-norm interpolation problems in Sobolev (Reproducing Kernel) Hilbert Spaces. Furthermore, the paper demonstrates a similar phenomenon in the multivariate-input case, showing that large-scale neural network learning is approximately equivalent to an ^2 minimization problem over a fixed kernel determined by optimal neurons. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how shallow ReLU neural networks work when they’re trying to solve many problems at once. They found out that the answers are really similar to what you get from using something called kernel regression. This is cool because it shows how these two things, neural networks and kernel methods, are connected. The researchers also showed that when you have a lot of tasks (problems) to solve, it’s like trying to find the shortest path in a special kind of math space. |
Keywords
» Artificial intelligence » Multi task » Neural network » Regression » Relu
