5 citations · 14 across the 4 of their papers we have counts for
6 papers
Generalization Through The Lens Of Leave-One-Out Error
Gregor Bachmann, Thomas Hofmann, Aurélien Lucchi
Despite the tremendous empirical success of deep learning models to solve various learning tasks, our theoretical understanding of their generalization ability is very limited. Cla…
Analytic Insights into Structure and Rank of Neural Network Hessian Maps
Sidak Pal Singh, Gregor Bachmann, Thomas Hofmann
The Hessian of a neural network captures parameter interactions through second-order derivatives of the loss. It is a fundamental object of study, closely tied to various problems…
Precise characterization of the prior predictive distribution of deep ReLU networks
Lorenzo Noci, Gregor Bachmann, Kevin Roth +2
Recent works on Bayesian neural networks (BNNs) have highlighted the need to better understand the implications of using Gaussian priors in combination with the compositional struc…
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect
Lorenzo Noci, Kevin Roth, Gregor Bachmann +2
The "cold posterior effect" (CPE) in Bayesian deep learning describes the uncomforting observation that the predictive performance of Bayesian neural networks can be significantly…
Uniform Convergence, Adversarial Spheres and a Simple Remedy
Gregor Bachmann, Seyed-Mohsen Moosavi-Dezfooli, Thomas Hofmann
Previous work has cast doubt on the general framework of uniform convergence and its ability to explain generalization in neural networks. By considering a specific dataset, it was…
Constant Curvature Graph Convolutional Networks
Gregor Bachmann, Gary Bécigneul, Octavian-Eugen Ganea
Interest has been rising lately towards methods representing data in non-Euclidean spaces, e.g. hyperbolic or spherical, that provide specific inductive biases useful for certain r…