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09/05/2023
Diffusion on the Probability Simplex
Diffusion models learn to reverse the progressive noising of a data dist...
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06/02/2023
Network Degeneracy as an Indicator of Training Performance: Comparing Finite and Infinite Width Angle Predictions
Neural networks are powerful functions with widespread use, but the theo...
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05/03/2023
Dynamic Sparse Training with Structured Sparsity
DST methods achieve state-of-the-art results in sparse neural network tr...
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02/20/2023
Depth Degeneracy in Neural Networks: Vanishing Angles in Fully Connected ReLU Networks on Initialization
Stacking many layers to create truly deep neural networks is arguably wh...
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07/19/2022
Bounding generalization error with input compression: An empirical study with infinite-width networks
Estimating the Generalization Error (GE) of Deep Neural Networks (DNNs) ...
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06/06/2022
The Neural Covariance SDE: Shaped Infinite Depth-and-Width Networks at Initialization
The logit outputs of a feedforward neural network at initialization are ...
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11/30/2021
Exponentially Tilted Gaussian Prior for Variational Autoencoder
An important propertyfor deep neural networks to possess is the ability ...
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10/16/2021
Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review
Scenario-based approaches have been receiving a huge amount of attention...
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06/07/2021
The Future is Log-Gaussian: ResNets and Their Infinite-Depth-and-Width Limit at Initialization
Theoretical results show that neural networks can be approximated by Gau...
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08/01/2020
Solving Elliptic Equations with Brownian Motion: Bias Reduction and Temporal Difference Learning
The Feynman-Kac formula provides a way to understand solutions to ellipt...
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01/17/2020
A Derivative-Free Method for Solving Elliptic Partial Differential Equations with Deep Neural Networks
We introduce a deep neural network based method for solving a class of e...
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09/13/2019
Finite Depth and Width Corrections to the Neural Tangent Kernel
We prove the precise scaling, at finite depth and width, for the mean an...
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12/14/2018
Products of Many Large Random Matrices and Gradients in Deep Neural Networks
We study products of random matrices in the regime where the number of t...
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11/15/2017
