46 citations · 106 across the 6 of their papers we have counts for
4 papers · 1 filter
A Study of the Learnability of Relational Properties: Model Counting Meets Machine Learning (MCML)
Muhammad Usman, Wenxi Wang, Kaiyuan Wang +3
This paper introduces the MCML approach for empirically studying the learnability of relational properties that can be expressed in the well-known software design language Alloy. A…
CRNs Exposed: Systematic Exploration of Chemical Reaction Networks
Marko Vasic, David Soloveichik, Sarfraz Khurshid
Formal methods have enabled breakthroughs in many fields, such as in hardware verification, machine learning and biological systems. The key object of interest in systems biology,…
MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning
Marko Vasic, Andrija Petrovic, Kaiyuan Wang +3
Rapid advancements in deep learning have led to many recent breakthroughs. While deep learning models achieve superior performance, often statistically better than humans, their ad…
Neural Program Repair by Jointly Learning to Localize and Repair
Marko Vasic, Aditya Kanade, Petros Maniatis +2
Due to its potential to improve programmer productivity and software quality, automated program repair has been an active topic of research. Newer techniques harness neural network…