Transgressing the boundaries: towards a rigorous understanding of deep learning and its (non-)robustness

5 July 2023

Papers citing "Transgressing the boundaries: towards a rigorous understanding of deep learning and its (non-)robustness"

7 / 7 papers shown

Title
Reliability and Interpretability in Science and Deep Learning Luigi Scorzato 28 3 0 14 Jan 2024
On the Convergence and Robustness of Adversarial Training Yisen Wang Xingjun Ma James Bailey Jinfeng Yi Bowen Zhou Quanquan Gu AAML 192 345 0 15 Dec 2021
VarGrad: A Low-Variance Gradient Estimator for Variational Inference Lorenz Richter Ayman Boustati Nikolas Nusken Francisco J. R. Ruiz Ömer Deniz Akyildiz DRL 127 48 0 20 Oct 2020
Deep neural network solution of the electronic Schrödinger equation J. Hermann Zeno Schätzle Frank Noé 149 446 0 16 Sep 2019
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 278 2,888 0 15 Sep 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 281 5,833 0 08 Jul 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 282 9,136 0 06 Jun 2015