--- title: "Who cites HolStep: A Machine Learning — 22 citing papers" description: "22 papers of the Elamur Research library cite «HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving», found in their…" canonical: "https://research.elamur.ai/free/en/papers/kaliszyk-2017-holstep-machine-learning-dataset-higher/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/kaliszyk-2017-holstep-machine-learning-dataset-higher/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [HolStep: A Machine Learning Dataset for…](https://md.research.elamur.ai/free/en/papers/kaliszyk-2017-holstep-machine-learning-dataset-higher/) / cited by # Who cites «HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving» 22 papers of our library cite it (of the 149,439 whose bibliographies we read) · 20 with a page here, the most cited first · [← back to «HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving»](https://md.research.elamur.ai/free/en/papers/kaliszyk-2017-holstep-machine-learning-dataset-higher/) 1. **[LeanDojo: Theorem Proving with Retrieval-Augmented Language Models](https://md.research.elamur.ai/free/en/papers/yang-2023-leandojo-theorem-proving-retrieval-augmented/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kaiyu Yang, Aidan M. Swope, Alex Gu, et al.*2023 · 253 citations* 2. [**End-to-End Differentiable Proving arXiv:1705.11040**Tim Rocktäschel, Sebastian Riedel*2017 · 188 citations*](https://md.research.elamur.ai/free/en/papers/rocktaschel-2017-end-end-differentiable-proving/) 3. [**Premise Selection for Theorem Proving by Deep Graph Embedding arXiv:1709.09994**Mingzhe Wang, Yihe Tang, Jian Wang, et al.*2017 · 106 citations*](https://md.research.elamur.ai/free/en/papers/wang-2017-premise-selection-theorem-proving-deep/) 4. [**GamePad: A Learning Environment for Theorem Proving arXiv:1806.00608**Daniel Huang, Prafulla Dhariwal, Dawn Song, et al.*2018 · 94 citations*](https://md.research.elamur.ai/free/en/papers/huang-2018-gamepad-learning-environment-theorem-proving/) 5. [**Proof Artifact Co-training for Theorem Proving with Language Models arXiv:2102.06203**Jesse Michael Han, Jason Rute, Yuhuai Wu, et al.*2021 · 82 citations*](https://md.research.elamur.ai/free/en/papers/han-2021-proof-artifact-co-training-theorem/) 6. [**Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers arXiv:2205.10893**Albert Q. Jiang, Wenda Li, Szymon Tworkowski, et al.*2022 · 79 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2022-thor-wielding-hammers-integrate-language/) 7. [**HOList: An Environment for Machine Learning of Higher-Order Theorem Proving arXiv:1904.03241**Kshitij Bansal, Sarah M. Loos, Markus N. Rabe, et al.*2019 · 78 citations*](https://md.research.elamur.ai/free/en/papers/bansal-2019-holist-environment-machine-learning-higher/) 8. [**Generating Correctness Proofs with Neural Networks arXiv:1907.07794**Alex Sanchez-Stern, Yousef Alhessi, Lawrence Saul, et al.*2019 · 69 citations*](https://md.research.elamur.ai/free/en/papers/stern-2019-generating-correctness-proofs-neural-networks/) 9. [**Formal Mathematical Reasoning: A New Frontier in AI arXiv:2412.16075**Kaiyu Yang, Gabriel Poesia, Jingxuan He, et al.*2024 · 49 citations*](https://md.research.elamur.ai/free/en/papers/yang-2024-formal-mathematical-reasoning-new-frontier/) 10. [**A Survey on Deep Learning for Theorem Proving arXiv:2404.09939**Zhaoyu Li, Jialiang Sun, Logan Murphy, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-survey-deep-learning-theorem-proving/) 11. [**IsarStep: a Benchmark for High-level Mathematical Reasoning arXiv:2006.09265**Wenda Li, Lei Yu, Yuhuai Wu, et al.*2020 · 43 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-isarstep-benchmark-high-level-mathematical/) 12. [**INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving arXiv:2007.02924**Yuhuai Wu, Albert Qiaochu Jiang, Jimmy Ba, et al.*2020 · 41 citations*](https://md.research.elamur.ai/free/en/papers/wu-2020-int-inequality-benchmark-evaluating-generalization/) 13. [**Lean Copilot: Large Language Models as Copilots for Theorem Proving in Lean arXiv:2404.12534**Peiyang Song, Kaiyu Yang, Anima Anandkumar*2024 · 39 citations*](https://md.research.elamur.ai/free/en/papers/song-2024-lean-copilot-large-language-models/) 14. [**Magnushammer: A Transformer-Based Approach to Premise Selection arXiv:2303.04488**Maciej Mikuła, Szymon Tworkowski, Szymon Antoniak, et al.*2023 · 35 citations*](https://md.research.elamur.ai/free/en/papers/mikula-2023-magnushammer-transformer-based-approach-premise/) 15. **[Property Invariant Embedding for Automated Reasoning](https://md.research.elamur.ai/free/en/papers/olsak-2019-property-invariant-embedding-automated-reasoning/) [Frontiers in artificial intelligence and applications](https://md.research.elamur.ai/free/en/venues/frontiers-in-artificial-intelligence-and-applications/)**Miroslav Olšák, Cezary Kaliszyk, Josef Urban*2019 · 30 citations* 16. [**Teaching Temporal Logics to Neural Networks arXiv:2003.04218**Christopher Hahn, Frederik Schmitt, Jens U. Kreber, et al.*2020 · 24 citations*](https://md.research.elamur.ai/free/en/papers/hahn-2020-teaching-temporal-logics-neural-networks/) 17. [**Guiding High-Performance SAT Solvers with Unsat-Core Predictions arXiv:1903.04671 (International Conference on Theory and Applications of Satisfiability Testing. Springer, Cham, 2019)**Daniel Selsam, Nikolaj Bjørner*2019 · 16 citations*](https://md.research.elamur.ai/free/en/papers/selsam-2019-guiding-high-performance-sat-solvers/) 18. **[Guiding Inferences in Connection Tableau by Recurrent Neural Networks](https://md.research.elamur.ai/free/en/papers/piotrowski-2019-guiding-inferences-connection-tableau-recurrent/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Bartosz Piotrowski, Josef Urban*2019 · 15 citations* 19. [**Automated Theorem Proving in Intuitionistic Propositional Logic by Deep Reinforcement Learning arXiv:1811.00796**Mitsuru Kusumoto, Keisuke Yahata, Masahiro Sakai*2018 · 15 citations*](https://md.research.elamur.ai/free/en/papers/kusumoto-2018-automated-theorem-proving-intuitionistic-propositional/) 20. [**Learning to Prove from Synthetic Theorems arXiv:2006.11259**Eser Aygün, Zafarali Ahmed, Ankit Anand, et al.*2020 · 12 citations*](https://md.research.elamur.ai/free/en/papers/aygun-2020-learning-prove-synthetic-theorems/) 2 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/kaliszyk-2017-holstep-machine-learning-dataset-higher/) — HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving