11 citations · 32 across the 9 of their papers we have counts for
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The Peril of Popular Deep Learning Uncertainty Estimation Methods
Yehao Liu, Matteo Pagliardini, Tatjana Chavdarova +1
Uncertainty estimation (UE) techniques -- such as the Gaussian process (GP), Bayesian neural networks (BNN), Monte Carlo dropout (MCDropout) -- aim to improve the interpretability…
Last-Iterate Convergence of Saddle-Point Optimizers via High-Resolution Differential Equations
Tatjana Chavdarova, Michael I. Jordan, Manolis Zampetakis
Several widely-used first-order saddle-point optimization methods yield an identical continuous-time ordinary differential equation (ODE) that is identical to that of the Gradient…
Semantic Perturbations with Normalizing Flows for Improved Generalization
Oguz Kaan Yuksel, Sebastian U. Stich, Martin Jaggi +1
Data augmentation is a widely adopted technique for avoiding overfitting when training deep neural networks. However, this approach requires domain-specific knowledge and is often…