183 citations · 435 across the 7 of their papers we have counts for
7 papers
An AI Approach to Verified Production Cryptographic Libraries
Chuyue Sun, Su Fong, Zhiyi Kuang +5
Cryptographic code is critical infrastructure that must be correct, yet formally verifying production libraries remains difficult. Existing language-model proof systems solve isola…
Resources: A Safe Language Abstraction for Money
Sam Blackshear, David L. Dill, Shaz Qadeer +4
Smart contracts are programs that implement potentially sophisticated transactions on modern blockchain platforms. In the rapidly evolving blockchain environment, smart contract pr…
Learning a SAT Solver from Single-Bit Supervision
Daniel Selsam, Matthew Lamm, Benedikt Bünz +3
We present NeuroSAT, a message passing neural network that learns to solve SAT problems after only being trained as a classifier to predict satisfiability. Although it is not compe…
Towards Proving the Adversarial Robustness of Deep Neural Networks
Guy Katz, Clark Barrett, David L. Dill +2
Autonomous vehicles are highly complex systems, required to function reliably in a wide variety of situations. Manually crafting software controllers for these vehicles is difficul…
Provably Minimally-Distorted Adversarial Examples
Nicholas Carlini, Guy Katz, Clark Barrett +1
The ability to deploy neural networks in real-world, safety-critical systems is severely limited by the presence of adversarial examples: slightly perturbed inputs that are misclas…
Developing Bug-Free Machine Learning Systems With Formal Mathematics
Daniel Selsam, Percy Liang, David L. Dill
Noisy data, non-convex objectives, model misspecification, and numerical instability can all cause undesired behaviors in machine learning systems. As a result, detecting actual im…