54 citations · 161 across the 5 of their papers we have counts for
7 papers · 1 filter
Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer
Greg Yang, Edward J. Hu, Igor Babuschkin +7
Hyperparameter (HP) tuning in deep learning is an expensive process, prohibitively so for neural networks (NNs) with billions of parameters. We show that, in the recently discovere…
Denoised Smoothing: A Provable Defense for Pretrained Classifiers
Hadi Salman, Mingjie Sun, Greg Yang +2
We present a method for provably defending any pretrained image classifier against adversarial attacks. This method, for instance, allows public vision API providers and u…
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…
A Fine-Grained Spectral Perspective on Neural Networks
Greg Yang, Hadi Salman
Are neural networks biased toward simple functions? Does depth always help learn more complex features? Is training the last layer of a network as good as training all layers? How…
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…
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks
Hadi Salman, Greg Yang, Huan Zhang +2
Verification of neural networks enables us to gauge their robustness against adversarial attacks. Verification algorithms fall into two categories: exact verifiers that run in expo…