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Who cites «Learning Continuous Semantic Representations of Symbolic Expressions»

17 papers of our library cite it (of the 149,439 whose bibliographies we read) · 11 with a page here, the most cited first · ← back to «Learning Continuous Semantic Representations of Symbolic Expressions»

  1. Relational inductive biases, deep learning, and graph networksarXiv:1806.01261Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, et al.2018 · 2,335 citations
  2. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelsarXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5BvosjAarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.2022 · 1,756 citations
  3. Analysing Mathematical Reasoning Abilities of Neural ModelsarXiv:1904.01557David Saxton, Edward Grefenstette, Felix Hill, et al.2019 · 288 citations
  4. A Survey of Machine Learning for Big Code and NaturalnessACM Computing SurveysMiltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, et al.2017 · 167 citations
  5. Deep Learning for Symbolic MathematicsarXiv:1912.01412Guillaume Lample, François Charton2019 · 138 citations
  6. Learning to Prove Theorems via Interacting with Proof AssistantsarXiv:1905.09381Kaiyu Yang, Jia Deng2019 · 90 citations
  7. Can Neural Networks Understand Logical Entailment?arXiv:1802.08535Richard Evans, David Saxton, David Amos, et al.2018 · 44 citations
  8. Deep Symbolic Regression for Recurrent SequencesarXiv:2201.04600Stéphane d'Ascoli, Pierre-Alexandre Kamienny, Guillaume Lample, et al.2022 · 27 citations
  9. Neuro-Symbolic Artificial Intelligence: Current TrendsarXiv:2105.05330Md Kamruzzaman Sarker, Lu Zhou, Aaron Eberhart, et al.2021 · 12 citations
  10. Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement LearningarXiv:1807.08058Gil Lederman, Markus N. Rabe, Edward A. Lee, et al.2018 · 12 citations

6 more papers of our library cite it; they have no page here yet.

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