Blake Bordelon | DeepAI

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Featured Co-authors

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Bryce Bagley
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∙ 07/10/2023

Dynamics of Temporal Difference Reinforcement Learning

Reinforcement learning has been successful across several applications i...

0 Blake Bordelon, et al. ∙

∙ 04/06/2023

Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks

We analyze the dynamics of finite width effects in wide but finite featu...

0 Blake Bordelon, et al. ∙

∙ 12/23/2022

The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes

For small training set sizes P, the generalization error of wide neural ...

0 Alexander Atanasov, et al. ∙

∙ 10/05/2022

The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks

It is unclear how changing the learning rule of a deep neural network al...

0 Blake Bordelon, et al. ∙

∙ 05/19/2022

Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks

We analyze feature learning in infinite width neural networks trained wi...

0 Blake Bordelon, et al. ∙

∙ 10/29/2021

Neural Networks as Kernel Learners: The Silent Alignment Effect

Neural networks in the lazy training regime converge to kernel machines....

0 Alexander Atanasov, et al. ∙

∙ 10/14/2021

Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?

Equivariance has emerged as a desirable property of representations of o...

0 Matthew Farrell, et al. ∙

∙ 06/04/2021

Learning Curves for SGD on Structured Features

The generalization performance of a machine learning algorithm such as a...

0 Blake Bordelon, et al. ∙

∙ 06/04/2021

Out-of-Distribution Generalization in Kernel Regression

In real word applications, data generating process for training a machin...

0 Abdulkadir Canatar, et al. ∙

∙ 05/29/2021

Rapid Feature Evolution Accelerates Learning in Neural Networks

Neural network (NN) training and generalization in the infinite-width li...

0 Haozhe Shan, et al. ∙

∙ 06/23/2020

Statistical Mechanics of Generalization in Kernel Regression

Generalization beyond a training dataset is a main goal of machine learn...

36 Abdulkadir Canatar, et al. ∙

∙ 02/07/2020

Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks

A fundamental question in modern machine learning is how deep neural net...

0 Blake Bordelon, et al. ∙

∙ 10/07/2018

Pre-Synaptic Pool Modification (PSPM): A Supervised Learning Procedure for Spiking Neural Networks

A central question in neuroscience is how to develop realistic models th...

0 Bryce Bagley, et al. ∙

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