Sarfraz Khurshid — accepted AI-conference papers

2024

NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks

Wenxi Wang, Yang Hu, Mohit Tiwari, Sarfraz Khurshid, Kenneth McMillan, Risto Miikkulainen

ICLR 2024poster

Propositional satisfiability (SAT) is an NP-complete problem that impacts many research fields, such as planning, verification, and security. Mainstream modern SAT solvers are based on the Conflict-Driven Clause Learning (CDCL) algorithm. Recent work aimed to enhance CDCL SAT solvers using Graph Neu…

Cited by 20 Source ↗PDF ↗code ↗Scholar ↗

2020

Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks

Marko Vasic, Cameron Chalk, Sarfraz Khurshid, David Soloveichik

ICML 2020poster

Embedding computation in molecular contexts incompatible with traditional electronics is expected to have wide ranging impact in synthetic biology, medicine, nanofabrication and other fields. A key remaining challenge lies in developing programming paradigms for molecular computation that are well-a…

Cited by 23 Source ↗PDF ↗Scholar ↗