372 citations · 567 across the 16 of their papers we have counts for
3 papers · 1 filter
Sorting out Lipschitz function approximation
Cem Anil, James Lucas, Roger Grosse
Training neural networks under a strict Lipschitz constraint is useful for provable adversarial robustness, generalization bounds, interpretable gradients, and Wasserstein distance…
Adversarial Distillation of Bayesian Neural Network Posteriors
Kuan-Chieh Wang, Paul Vicol, James Lucas +3
Bayesian neural networks (BNNs) allow us to reason about uncertainty in a principled way. Stochastic Gradient Langevin Dynamics (SGLD) enables efficient BNN learning by drawing sam…
Aggregated Momentum: Stability Through Passive Damping
James Lucas, Shengyang Sun, Richard Zemel +1
Momentum is a simple and widely used trick which allows gradient-based optimizers to pick up speed along low curvature directions. Its performance depends crucially on a damping co…