6 code implementations • ICLR 2019 • Daniel Selsam, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura, David L. Dill
We present NeuroSAT, a message passing neural network that learns to solve SAT problems after only being trained as a classifier to predict satisfiability.
no code implementations • ICLR 2018 • Nicholas Carlini, Guy Katz, Clark Barrett, David L. Dill
We demonstrate how ground truths can serve to assess the effectiveness of attack techniques, by comparing the adversarial examples produced by those attacks to the ground truths; and also of defense techniques, by computing the distance to the ground truths before and after the defense is applied, and measuring the improvement.
1 code implementation • 29 Sep 2017 • Nicholas Carlini, Guy Katz, Clark Barrett, David L. Dill
Using this approach, we demonstrate that one of the recent ICLR defense proposals, adversarial retraining, provably succeeds at increasing the distortion required to construct adversarial examples by a factor of 4. 2.
no code implementations • 8 Sep 2017 • Guy Katz, Clark Barrett, David L. Dill, Kyle Julian, Mykel J. Kochenderfer
Autonomous vehicles are highly complex systems, required to function reliably in a wide variety of situations.
1 code implementation • ICML 2017 • Daniel Selsam, Percy Liang, David L. Dill
As a case study, we implement a new system, Certigrad, for optimizing over stochastic computation graphs, and we generate a formal (i. e. machine-checkable) proof that the gradients sampled by the system are unbiased estimates of the true mathematical gradients.