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Mitigating the Curse of Detail: Scaling Arguments for Feature Learning and Sample Complexity
Noa Rubin, Orit Davidovich, Zohar Ringel
Two pressing topics in the theory of deep learning are the interpretation of feature learning (FL) mechanisms and the determination of implicit bias of networks in the rich regime.…
Renormalization group for deep neural networks: Universality of learning and scaling laws
Gorka Peraza Coppola, Moritz Helias, Zohar Ringel
Self-similarity, where observables at different length scales exhibit similar behavior, is ubiquitous in natural systems. Such systems are typically characterized by power-law corr…
Applications of Statistical Field Theory in Deep Learning
Zohar Ringel, Noa Rubin, Edo Mor +2
Deep learning algorithms have made incredible strides in the past decade, yet due to their complexity, the science of deep learning remains in its early stages. Being an experiment…
From Kernels to Features: A Multi-Scale Adaptive Theory of Feature Learning
Noa Rubin, Kirsten Fischer, Javed Lindner +5
Feature learning in neural networks is crucial for their expressive power and inductive biases, motivating various theoretical approaches. Some approaches describe network behavior…