261 citations · 836 across the 30 of their papers we have counts for
5 papers · 1 filter
Inductive Bias of Multi-Channel Linear Convolutional Networks with Bounded Weight Norm
Meena Jagadeesan, Ilya Razenshteyn, Suriya Gunasekar
We provide a function space characterization of the inductive bias resulting from minimizing the norm of the weights in multi-channel convolutional neural networks with li…
Randomized Smoothing of All Shapes and Sizes
Greg Yang, Tony Duan, J. Edward Hu +3
Randomized smoothing is the current state-of-the-art defense with provable robustness against adversarial attacks. Many works have devised new randomized smoothing schemes…
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
Hadi Salman, Greg Yang, Jerry Li +4
Recent works have shown the effectiveness of randomized smoothing as a scalable technique for building neural network-based classifiers that are provably robust to -norm ad…
Learning Space Partitions for Nearest Neighbor Search
Yihe Dong, Piotr Indyk, Ilya Razenshteyn +1
Space partitions of underlie a vast and important class of fast nearest neighbor search (NNS) algorithms. Inspired by recent theoretical work on NNS for general metr…
Adversarial Examples from Cryptographic Pseudo-Random Generators
Sébastien Bubeck, Yin Tat Lee, Eric Price +1
In our recent work (Bubeck, Price, Razenshteyn, arXiv:1805.10204) we argued that adversarial examples in machine learning might be due to an inherent computational hardness of the…