Demystifying Deep Learning Compiler Frontend Bugs: An LLM-Aided Empirical Study | Paper Citation Record

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This paper cites Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng.

Demystifying Deep Learning Compiler Frontend Bugs: An LLM-Aided Empirical Study Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng

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Demystifying Deep Learning Compiler Frontend Bugs: An LLM-Aided Empirical Study Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning

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Demystifying Deep Learning Compiler Frontend Bugs: An LLM-Aided Empirical Study Liang, Carmen Badea, Christian Bird, Robert DeLine, Denae Ford, Nicole Forsgren, and Thomas Zimmermann

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Demystifying Deep Learning Compiler Frontend Bugs: An LLM-Aided Empirical Study Unresolved cited work

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Demystifying Deep Learning Compiler Frontend Bugs: An LLM-Aided Empirical Study Unresolved cited work

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Demystifying Deep Learning Compiler Frontend Bugs: An LLM-Aided Empirical Study arXiv:2510.04997 [cs.SE] https://arxiv.org/abs/2510.04997

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Demystifying Deep Learning Compiler Frontend Bugs: An LLM-Aided Empirical Study Unresolved cited work

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Demystifying Deep Learning Compiler Frontend Bugs: An LLM-Aided Empirical Study Unresolved cited work

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Demystifying Deep Learning Compiler Frontend Bugs: An LLM-Aided Empirical Study InProceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation(Virtual, Canada)(PLDI 2021)

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Demystifying Deep Learning Compiler Frontend Bugs: An LLM-Aided Empirical Study Engg.29, 1 (Nov

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Coverage vector

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Pith citing papers