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Creators/Authors contains: "Singh, Mayank"

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  1. ; ; ; ; (, IEEE)
    Free, publicly-accessible full text available May 19, 2026
  2. ; ; ; (, IEEE Transactions on Neural Systems and Rehabilitation Engineering)
    Free, publicly-accessible full text available January 1, 2026
  3. ; (, Findings of the Association for Computational Linguistics (EMNLP 2024))
    We propose a neuro-symbolic approach for realistic few-shot relation classification via rules. Instead of building neural models to predict relations, we design them to output straight- forward rules that can be used to extract relations. The rules are generated using custom T5-style Encoder-Decoder Language Models. Crucially, our rules are fully interpretable and pliable (i.e., humans can easily modify them to boost performance). Through a combination of rules generated by these models along with a very effective, novel baseline, we demonstrate a few-shot relation-classification performance that is comparable to or stronger than the state of the art on the Few-Shot TACRED and NYT29 benchmarks while increasing interpretability and maintaining pliability. 
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    Free, publicly-accessible full text available November 12, 2025
  4. ; ; ; ; ; (, IEEE Transactions on Neural Systems and Rehabilitation Engineering)
    Free, publicly-accessible full text available January 1, 2026
  5. ; ; ; ; ; (, IEEE Transactions on Robotics)
    Full Text Available 
  6. ; (, IEEE)
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  7. ; ; (, IEEE)
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  8. ; ; (, IEEE)
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  9. ; ; ; (, 2022 IEEE Conference on Control Technology and Applications (CCTA))
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  10. ; ; ; ; (, 2022 IEEE Conference on Control Technology and Applications (CCTA))
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