Linear Classification of Neural Manifolds with Correlated Variability
Author:
Publisher
American Physical Society (APS)
Subject
General Physics and Astronomy
Link
http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevLett.131.027301/fulltext
Reference42 articles.
1. Counting the learnable functions of geometrically structured data
2. Capacity-Resolution Trade-Off in the Optimal Learning of Multiple Low-Dimensional Manifolds by Attractor Neural Networks
3. Modeling the Influence of Data Structure on Learning in Neural Networks: The Hidden Manifold Model
4. Geometric framework to predict structure from function in neural networks
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1. Inversion dynamics of class manifolds in deep learning reveals tradeoffs underlying generalization;Nature Machine Intelligence;2024-01-08
2. A statistical mechanics framework for Bayesian deep neural networks beyond the infinite-width limit;Nature Machine Intelligence;2023-12-18
3. Linear Classification of Neural Manifolds with Correlated Variability;Physical Review Letters;2023-07-12
4. Performance Capacity of a Complex Neural Network;Physics;2023-07-12
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