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02/18/2022
SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions
Automatic machine learning, or AutoML, holds the promise of truly democr...
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10/26/2021
NeuroComb: Improving SAT Solving with Graph Neural Networks
Propositional satisfiability (SAT) is an NP-complete problem that impact...
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09/20/2021
Programming and Training Rate-Independent Chemical Reaction Networks
Embedding computation in biochemical environments incompatible with trad...
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06/03/2021
Distributed Symbolic Execution using Test-Depth Partitioning
Symbolic execution is a classic technique for systematic bug finding, wh...
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03/30/2020
Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks
Embedding computation in molecular contexts incompatible with traditiona...
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12/25/2019
A Study of the Learnability of Relational Properties (Model Counting Meets Machine Learning)
Relational properties, e.g., the connectivity structure of nodes in a di...
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06/16/2019
MoËT: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees
Deep Reinforcement Learning (DRL) has led to many recent breakthroughs o...
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08/06/2018
Executable Trigger-Action Comments
Natural language elements, e.g., todo comments, are frequently used to c...
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07/27/2018
Symbolic Execution for Deep Neural Networks
Deep Neural Networks (DNN) are increasingly used in a variety of applica...
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07/23/2018
Fault Localization for Declarative Models in Alloy
Fault localization is a popular research topic and many techniques have ...
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02/07/2018
DeepRoad: GAN-based Metamorphic Autonomous Driving System Testing
While Deep Neural Networks (DNNs) have established the fundamentals of D...
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09/05/2017
