24 citations · 115 across the 11 of their papers we have counts for
3 papers · 1 filter
How does training shape the Riemannian geometry of neural network representations?
Jacob A. Zavatone-Veth, Sheng Yang, Julian A. Rubinfien +1
In machine learning, there is a long history of trying to build neural networks that can learn from fewer example data by baking in strong geometric priors. However, it is not alwa…
Contrasting random and learned features in deep Bayesian linear regression
Jacob A. Zavatone-Veth, William L. Tong, Cengiz Pehlevan
Understanding how feature learning affects generalization is among the foremost goals of modern deep learning theory. Here, we study how the ability to learn representations affect…
Depth induces scale-averaging in overparameterized linear Bayesian neural networks
Jacob A. Zavatone-Veth, Cengiz Pehlevan
Inference in deep Bayesian neural networks is only fully understood in the infinite-width limit, where the posterior flexibility afforded by increased depth washes out and the post…