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07/27/2023
Speed Limits for Deep Learning
State-of-the-art neural networks require extreme computational power to ...
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07/12/2023
Spectral-Bias and Kernel-Task Alignment in Physically Informed Neural Networks
Physically informed neural networks (PINNs) are a promising emerging met...
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12/31/2021
Separation of scales and a thermodynamic description of feature learning in some CNNs
Deep neural networks (DNNs) are powerful tools for compressing and disti...
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06/08/2021
A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs
Deep neural networks (DNNs) in the infinite width/channel limit have rec...
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12/02/2020
Relevance in the Renormalization Group and in Information Theory
The analysis of complex physical systems hinges on the ability to extrac...
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04/02/2020
Predicting the outputs of finite networks trained with noisy gradients
A recent line of studies has focused on the infinite width limit of deep...
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06/12/2019
Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective
A series of recent works suggest that deep neural networks (DNNs), of fi...
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02/06/2019
The role of a layer in deep neural networks: a Gaussian Process perspective
A fundamental question in deep learning concerns the role played by indi...
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02/06/2018
