Ben Adlam | DeepAI

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∙ 03/09/2023

Kernel Regression with Infinite-Width Neural Networks on Millions of Examples

Neural kernels have drastically increased performance on diverse and non...

0 Ben Adlam, et al. ∙

∙ 06/21/2022

Ensembling over Classifiers: a Bias-Variance Perspective

Ensembles are a straightforward, remarkably effective method for improvi...

0 Neha Gupta, et al. ∙

∙ 06/15/2022

Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions

Stochastic gradient descent (SGD) is a pillar of modern machine learning...

0 Courtney Paquette, et al. ∙

∙ 05/14/2022

Homogenization of SGD in high-dimensions: Exact dynamics and generalization properties

We develop a stochastic differential equation, called homogenized SGD, f...

0 Courtney Paquette, et al. ∙

∙ 02/08/2022

Understanding the bias-variance tradeoff of Bregman divergences

This paper builds upon the work of Pfau (2013), which generalized the bi...

0 Ben Adlam, et al. ∙

∙ 11/16/2021

Covariate Shift in High-Dimensional Random Feature Regression

A significant obstacle in the development of robust machine learning mod...

0 Nilesh Tripuraneni, et al. ∙

∙ 11/06/2020

Underspecification Presents Challenges for Credibility in Modern Machine Learning

ML models often exhibit unexpectedly poor behavior when they are deploye...

30 Alexander D'Amour, et al. ∙

∙ 11/04/2020

Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition

Classical learning theory suggests that the optimal generalization perfo...

0 Ben Adlam, et al. ∙

∙ 10/14/2020

Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit

Modern deep learning models have achieved great success in predictive ac...

7 Ben Adlam, et al. ∙

∙ 08/15/2020

The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization

Modern deep learning models employ considerably more parameters than req...

0 Ben Adlam, et al. ∙

∙ 07/31/2020

Cold Posteriors and Aleatoric Uncertainty

Recent work has observed that one can outperform exact inference in Baye...

0 Ben Adlam, et al. ∙

∙ 07/31/2020

Finite Versus Infinite Neural Networks: an Empirical Study

We perform a careful, thorough, and large scale empirical study of the c...

49 Jaehoon Lee, et al. ∙

∙ 12/02/2019

A Random Matrix Perspective on Mixtures of Nonlinearities for Deep Learning

One of the distinguishing characteristics of modern deep learning system...

0 Ben Adlam, et al. ∙

∙ 10/30/2019

Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks

We investigate under and overfitting in Generative Adversarial Networks ...

0 Ben Adlam, et al. ∙

∙ 10/20/2019

Learning GANs and Ensembles Using Discrepancy

Generative adversarial networks (GANs) generate data based on minimizing...

30 Ben Adlam, et al. ∙

∙ 04/30/2019

AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles

AdaNet is a lightweight TensorFlow-based (Abadi et al., 2015) framework ...

38 Charles Weill, et al. ∙

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