Collective variables of neural networks: empirical time evolution and scaling laws
This work presents a novel framework for understanding learning dynamics and scaling relations in neural networks. We show that certain measures on the spectrum of the empirical neural tangent kernel (NTK), specifically entropy and trace, provide insight into the representations learned by a neural...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/adee76 |
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