Temporal correlations from lazy random walks enable efficient SGD learning of k-juntas via temporal-difference loss on ReLU networks, achieving linear sample complexity in d.
Approximation and Learning with Deep Convolutional Models: a Kernel Perspective , publisher =
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Review of neural scaling laws and their relation to constraints and inductive biases when applying machine learning to physics problems.
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Statistical Properties of Training & Generalization
Review of neural scaling laws and their relation to constraints and inductive biases when applying machine learning to physics problems.