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David L. Dill

2 accepted papers

2019

Learning a SAT Solver from Single-Bit Supervision

ICLR 2019poster

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 competitive with state-of-the-art SAT solvers, NeuroSAT can solve problems that are substantially larger and more difficult…

Cited by 541SourcePDFScholar
2017

Developing Bug-Free Machine Learning Systems With Formal Mathematics

ICML 2017poster

Noisy data, non-convex objectives, model misspecification, and numerical instability can all cause undesired behaviors in machine learning systems. As a result, detecting actual implementation errors can be extremely difficult. We demonstrate a methodology in which developers use an interactive proo…