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Found 5 papers, 3 papers with code
Learning a SAT Solver from Single-Bit Supervision
We present NeuroSAT, a message passing neural network that learns to solve SAT problems after only being trained as a classifier to predict satisfiability.
Ground-Truth Adversarial Examples
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.
Provably Minimally-Distorted Adversarial Examples
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.
Towards Proving the Adversarial Robustness of Deep Neural Networks
Autonomous vehicles are highly complex systems, required to function reliably in a wide variety of situations.
Developing Bug-Free Machine Learning Systems With Formal Mathematics
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.
