430 citations · 573 across the 9 of their papers we have counts for
11 papers
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Alexander D'Amour, Katherine Heller, Dan Moldovan +37
ML models often exhibit unexpectedly poor behavior when they are deployed in real-world domains. We identify underspecification as a key reason for these failures. An ML pipeline i…
Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition
Ben Adlam, Jeffrey Pennington
Classical learning theory suggests that the optimal generalization performance of a machine learning model should occur at an intermediate model complexity, with simpler models exh…
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit
Ben Adlam, Jaehoon Lee, Lechao Xiao +2
Modern deep learning models have achieved great success in predictive accuracy for many data modalities. However, their application to many real-world tasks is restricted by poor u…
The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization
Ben Adlam, Jeffrey Pennington
Modern deep learning models employ considerably more parameters than required to fit the training data. Whereas conventional statistical wisdom suggests such models should drastica…
Cold Posteriors and Aleatoric Uncertainty
Ben Adlam, Jasper Snoek, Samuel L. Smith
Recent work has observed that one can outperform exact inference in Bayesian neural networks by tuning the "temperature" of the posterior on a validation set (the "cold posterior"…
Finite Versus Infinite Neural Networks: an Empirical Study
Jaehoon Lee, Samuel S. Schoenholz, Jeffrey Pennington +4
We perform a careful, thorough, and large scale empirical study of the correspondence between wide neural networks and kernel methods. By doing so, we resolve a variety of open que…