A syntax-guided edit decoder for neural program repair
Automated Program Repair (APR) helps improve the efficiency of software development and
maintenance. Recent APR techniques use deep learning, particularly the encoder-decoder …
maintenance. Recent APR techniques use deep learning, particularly the encoder-decoder …
An overview of machine learning techniques in constraint solving
Constraint solving is applied in different application contexts. Examples thereof are the
configuration of complex products and services, the determination of production schedules …
configuration of complex products and services, the determination of production schedules …
Machine learning methods in solving the boolean satisfiability problem
This paper reviews the recent literature on solving the Boolean satisfiability problem (SAT),
an archetypal NP-complete problem, with the aid of machine learning (ML) techniques. Over …
an archetypal NP-complete problem, with the aid of machine learning (ML) techniques. Over …
Hardsatgen: Understanding the difficulty of hard sat formula generation and a strong structure-hardness-aware baseline
Industrial SAT formula generation is a critical yet challenging task. Existing SAT generation
approaches can hardly simultaneously capture the global structural properties and maintain …
approaches can hardly simultaneously capture the global structural properties and maintain …
A state-of-the-art review on the utilization of machine learning in nanofluids, solar energy generation, and the prognosis of solar power
In the contemporary data-driven era, the fields of machine learning, deep learning, big data,
statistics, and data science are essential for forecasting outcomes and getting insights from …
statistics, and data science are essential for forecasting outcomes and getting insights from …
On EDA-Driven Learning for SAT Solving
We present DeepSAT, a novel end-to-end learning framework for the Boolean satisfiability
(SAT) problem. Unlike existing solutions trained on random SAT instances with relatively …
(SAT) problem. Unlike existing solutions trained on random SAT instances with relatively …
One model, any csp: Graph neural networks as fast global search heuristics for constraint satisfaction
We propose a universal Graph Neural Network architecture which can be trained as an end-
2-end search heuristic for any Constraint Satisfaction Problem (CSP). Our architecture can …
2-end search heuristic for any Constraint Satisfaction Problem (CSP). Our architecture can …
NSNet: A general neural probabilistic framework for satisfiability problems
Z Li, X Si - Advances in Neural Information Processing …, 2022 - proceedings.neurips.cc
Abstract We present the Neural Satisfiability Network (NSNet), a general neural framework
that models satisfiability problems as probabilistic inference and meanwhile exhibits proper …
that models satisfiability problems as probabilistic inference and meanwhile exhibits proper …
A cerebellar operant conditioning-inspired constraint satisfaction approach for product design concept generation
Conceptual design is a pivotal stage of new product development. The function-behaviour-
structure framework is adopted in this stage to help designers search design space and …
structure framework is adopted in this stage to help designers search design space and …
Survey of machine learning for software-assisted hardware design verification: Past, present, and prospect
With the ever-increasing hardware design complexity comes the realization that efforts
required for hardware verification increase at an even faster rate. Driven by the push from …
required for hardware verification increase at an even faster rate. Driven by the push from …