70 citations · 213 across the 19 of their papers we have counts for
22 papers
Faithful Knowledge Distillation
Tom A. Lamb, Rudy Brunel, Krishnamurthy DJ Dvijotham +3
Knowledge distillation (KD) has received much attention due to its success in compressing networks to allow for their deployment in resource-constrained systems. While the problem…
Expressive Losses for Verified Robustness via Convex Combinations
Alessandro De Palma, Rudy Bunel, Krishnamurthy Dvijotham +3
In order to train networks for verified adversarial robustness, it is common to over-approximate the worst-case loss over perturbation regions, resulting in networks that attain ve…
Efficient Error Certification for Physics-Informed Neural Networks
Francisco Eiras, Adel Bibi, Rudy Bunel +3
Recent work provides promising evidence that Physics-Informed Neural Networks (PINN) can efficiently solve partial differential equations (PDE). However, previous works have failed…
A Stochastic Bundle Method for Interpolating Networks
Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel +1
We propose a novel method for training deep neural networks that are capable of interpolation, that is, driving the empirical loss to zero. At each iteration, our method constructs…
Learning to be adversarially robust and differentially private
Jamie Hayes, Borja Balle, M. Pawan Kumar
We study the difficulties in learning that arise from robust and differentially private optimization. We first study convergence of gradient descent based adversarial training with…
Improving Local Effectiveness for Global robust training
Jingyue Lu, M. Pawan Kumar
Despite its popularity, deep neural networks are easily fooled. To alleviate this deficiency, researchers are actively developing new training strategies, which encourage models th…