Summary of Exploring the Manifold Of Neural Networks Using Diffusion Geometry, by Elliott Abel et al.
Exploring the Manifold of Neural Networks Using Diffusion Geometry
by Elliott Abel, Andrew J. Steindl, Selma Mazioud, Ellie Schueler, Folu Ogundipe, Ellen Zhang, Yvan Grinspan, Kristof Reimann, Peyton Crevasse, Dhananjay Bhaskar, Siddharth Viswanath, Yanlei Zhang, Tim G. J. Rudner, Ian Adelstein, Smita Krishnaswamy
First submitted to arxiv on: 19 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 paper applies manifold learning to the space of neural networks, inspired by the manifold hypothesis. By introducing a distance between hidden layer representations, it learns manifolds where datapoints are neural networks using PHATE, a non-linear dimensionality reduction algorithm. The resulting manifold is characterized by features such as class separation, hierarchical cluster structure, spectral entropy, and topological structure. Analysis reveals that high-performing networks cluster together, displaying consistent embedding patterns across these features. This approach can be used to guide hyperparameter optimization and neural architecture search by sampling from the manifold. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper takes a new approach to understanding neural networks. It looks at all the different types of neural networks and tries to group them together in a way that makes sense. By doing this, it can figure out what makes some neural networks better than others and how to make even better ones. This is important because it could help us learn more about how neural networks work and how we can use them for things like image recognition or natural language processing. |
Keywords
» Artificial intelligence » Dimensionality reduction » Embedding » Hyperparameter » Manifold learning » Natural language processing » Optimization
