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09/17/2023
On the Connection Between Riemann Hypothesis and a Special Class of Neural Networks
The Riemann hypothesis (RH) is a long-standing open problem in mathemati...
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02/14/2023
Data pruning and neural scaling laws: fundamental limitations of score-based algorithms
Data pruning algorithms are commonly used to reduce the memory and compu...
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02/01/2023
Width and Depth Limits Commute in Residual Networks
We show that taking the width and depth to infinity in a deep neural net...
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10/03/2022
On the infinite-depth limit of finite-width neural networks
In this paper, we study the infinite-depth limit of finite-width residua...
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02/22/2022
Connecting Optimization and Generalization via Gradient Flow Path Length
Optimization and generalization are two essential aspects of machine lea...
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10/22/2021
Probabilistic fine-tuning of pruning masks and PAC-Bayes self-bounded learning
We study an approach to learning pruning masks by optimizing the expecte...
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10/22/2021
The Equilibrium Hypothesis: Rethinking implicit regularization in Deep Neural Networks
Modern Deep Neural Networks (DNNs) exhibit impressive generalization pro...
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06/06/2021
Regularization in ResNet with Stochastic Depth
Regularization plays a major role in modern deep learning. From classic ...
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10/24/2020
Stable ResNet
Deep ResNet architectures have achieved state of the art performance on ...
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02/19/2020
Pruning untrained neural networks: Principles and Analysis
Overparameterized neural networks display state-of-the art performance. ...
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05/31/2019
Training Dynamics of Deep Networks using Stochastic Gradient Descent via Neural Tangent Kernel
Stochastic Gradient Descent (SGD) is widely used to train deep neural ne...
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02/19/2019
On the Impact of the Activation Function on Deep Neural Networks Training
The weight initialization and the activation function of deep neural net...
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05/21/2018
