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Found 8 papers, 2 papers with code
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.
An Augmented Translation Technique for low Resource language pair: Sanskrit to Hindi translation
Subsequently the same architecture is tested for Sanskrit to Hindi translation for which data is sparse, by training the model on English-Hindi and Sanskrit-English language pairs.
Common Representation Learning Using Step-based Correlation Multi-Modal CNN
In a broader perspective, the techniques used to investigate common representation learning falls under the categories of canonical correlation-based approaches and autoencoder based approaches.
