From Kernels to Features: A Multi-Scale Adaptive Theory of Feature Learning
Rubin, Noa; Fischer, Kirsten; Lindner, Javed; Dahmen, David; Seroussi, Inbar; Ringel, Zohar; Krämer, Michael; +1 Authors
Rubin, Noa; Fischer, Kirsten; Lindner, Javed; Dahmen, David; Seroussi, Inbar; Ringel, Zohar; Krämer, Michael; Helias, Moritz;
From Kernels to Features: A Multi-Scale Adaptive Theory of Feature Learning
Code for From Kernels to Features: A Multi-Scale Adaptive Theory of Feature Learning This repository contains the code accompanying the paper: Rubin, N., Fischer, K., Lindner, J., Dahmen, D., Seroussi, I., Ringel, Z., Krämer, M., Helias, M. From Kernels to Features: A Multi-Scale Adaptive Theory of Feature Learning (arxiv 2502.03210). For any questions, please contact Noa Rubin (noa.rubin@mail.huji.ac.il), Kirsten Fischer (ki.fischer@fz-juelich.de) or Javed Lindner (javed.lindner@rwth-aachen.de).
feature learning, deep neural networks, Bayesian inference, kernel adaptation, field theory, kernel rescaling
feature learning, deep neural networks, Bayesian inference, kernel adaptation, field theory, kernel rescaling
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selected citations
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These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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