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Found 22 papers, 7 papers with code
Online Bayesian Moment Matching based SAT Solver Heuristics
Our solvers out-perform the baselines by solving 12 more instances from the SAT competition 2018 application benchmark and are %40 faster on average in solving hard cryptographic instances.
A Reinforcement Learning based Reset Policy for CDCL SAT Solvers
In most solvers, variable activities are preserved across restart boundaries, resulting in solvers continuing to search parts of the assignment tree that are not far from the one immediately prior to a restart.
Layered and Staged Monte Carlo Tree Search for SMT Strategy Synthesis
Our method treats strategy synthesis as a sequential decision-making process, whose search tree corresponds to the strategy space, and employs MCTS to navigate this vast search space.
AlphaMapleSAT: An MCTS-based Cube-and-Conquer SAT Solver for Hard Combinatorial Problems
We perform an extensive comparison of AlphaMapleSAT against the March CnC solver on challenging combinatorial problems such as the minimum Kochen-Specker and Ramsey problems.
CoTran: An LLM-based Code Translator using Reinforcement Learning with Feedback from Compiler and Symbolic Execution
Also, built upon CodeT5, CoTran achieves +11. 23%, +14. 89% improvement on FEqAcc and +4. 07%, +8. 14% on CompAcc for Java-to-Python and Python-to-Java translation resp.
BertRLFuzzer: A BERT and Reinforcement Learning Based Fuzzer
We present a novel tool BertRLFuzzer, a BERT and Reinforcement Learning (RL) based fuzzer aimed at finding security vulnerabilities for Web applications.
CGDTest: A Constrained Gradient Descent Algorithm for Testing Neural Networks
This functionality sets CGDTest apart from other similar DNN testing tools since it allows users to specify logical constraints to test DNNs not only for $\ell_p$ ball-based adversarial robustness but, more importantly, includes richer properties such as disguised and flow adversarial constraints, as well as adversarial robustness in the NLP domain.
Learning Modulo Theories
Recent techniques that integrate \emph{solver layers} into Deep Neural Networks (DNNs) have shown promise in bridging a long-standing gap between inductive learning and symbolic reasoning techniques.
A Solver + Gradient Descent Training Algorithm for Deep Neural Networks
We present a novel hybrid algorithm for training Deep Neural Networks that combines the state-of-the-art Gradient Descent (GD) method with a Mixed Integer Linear Programming (MILP) solver, outperforming GD and variants in terms of accuracy, as well as resource and data efficiency for both regression and classification tasks.
String Theories involving Regular Membership Predicates: From Practice to Theory and Back
Designing an algorithm for the (generally undecidable) satisfiability problem for systems of string constraints requires a thorough understanding of the structure of constraints present in the targeted cases.
A SAT-based Resolution of Lam's Problem
In 1989, computer searches by Lam, Thiel, and Swiercz experimentally resolved Lam's problem from projective geometry$\unicode{x2014}$the long-standing problem of determining if a projective plane of order ten exists.
Logic Guided Genetic Algorithms
Our method, logic-guided genetic algorithm (LGGA), takes as input a set of labelled data points and auxiliary truths (ATs) (mathematical facts known a priori about the unknown function the regressor aims to learn) and outputs a specially generated and curated dataset that can be used with any SR method.
Amnesiac Machine Learning
In this paper, we present two efficient methods that address this question of how a model owner or data holder may delete personal data from models in such a way that they may not be vulnerable to model inversion and membership inference attacks while maintaining model efficacy.
LGML: Logic Guided Machine Learning
We introduce Logic Guided Machine Learning (LGML), a novel approach that symbiotically combines machine learning (ML) and logic solvers with the goal of learning mathematical functions from data.
Discovering Symmetry Invariants and Conserved Quantities by Interpreting Siamese Neural Networks
It turns out that in the process of learning which datapoints belong to the same event or field configuration, these SNNs also learn the relevant symmetry invariants and conserved quantities.
xAI-GAN: Enhancing Generative Adversarial Networks via Explainable AI Systems
A potential weakness in GANs is that it requires a lot of data for successful training and data collection can be an expensive process.
MPro: Combining Static and Symbolic Analysis for Scalable Testing of Smart Contract
We have implemented our technique in a tool called MPro, a scalable and automated smart contract analyzer based on the existing symbolic analysis tool Mythril-Classic and the static analysis tool Slither.
Cryptography and Security
An Empirical Investigation of Randomized Defenses against Adversarial Attacks
In this paper, we propose a scientific evaluation methodology aimed at assessing the quality, efficacy, robustness and efficiency of randomized defenses to protect DNNs against adversarial examples.
SAT Solvers and Computer Algebra Systems: A Powerful Combination for Mathematics
By combining the search power of SAT with the deep mathematical knowledge in CASs we can solve many problems in mathematics that no other known methods seem capable of solving.
Effective problem solving using SAT solvers
In this article we demonstrate how to solve a variety of problems and puzzles using the built-in SAT solver of the computer algebra system Maple.
Relating Complexity-theoretic Parameters with SAT Solver Performance
Over the years complexity theorists have proposed many structural parameters to explain the surprising efficiency of conflict-driven clause-learning (CDCL) SAT solvers on a wide variety of large industrial Boolean instances.
SAT-based Analysis of Large Real-world Feature Models is Easy
We discovered that a key reason why large real-world FMs are easy-to-analyze is that the vast majority of the variables in these models are unrestricted, i. e., the models are satisfiable for both true and false assignments to such variables under the current partial assignment.
