--- title: "Who cites Premise Selection for Theorem — 34 citing papers" description: "34 papers of the Elamur Research library cite «Premise Selection for Theorem Proving by Deep Graph Embedding», found in their…" canonical: "https://research.elamur.ai/free/en/papers/wang-2017-premise-selection-theorem-proving-deep/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wang-2017-premise-selection-theorem-proving-deep/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Premise Selection for Theorem Proving by Deep…](https://md.research.elamur.ai/free/en/papers/wang-2017-premise-selection-theorem-proving-deep/) / cited by # Who cites «Premise Selection for Theorem Proving by Deep Graph Embedding» 34 papers of our library cite it (of the 149,439 whose bibliographies we read) · 23 with a page here, the most cited first · [← back to «Premise Selection for Theorem Proving by Deep Graph Embedding»](https://md.research.elamur.ai/free/en/papers/wang-2017-premise-selection-theorem-proving-deep/) 1. **[oLMpics — On what Language Model Pre-training Captures](https://md.research.elamur.ai/free/en/papers/talmor-2019-olmpics-language-model-pre-training/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Alon Talmor, Yanai Elazar, Yoav Goldberg, et al.*2019 · 440 citations* 2. [**Generative Language Modeling for Automated Theorem Proving arXiv:2009.03393**Stanislas Polu, Ilya Sutskever*2020 · 241 citations*](https://md.research.elamur.ai/free/en/papers/polu-2020-generative-language-modeling-automated-theorem/) 3. [**Formal Mathematics Statement Curriculum Learning arXiv:2202.01344**Stanislas Polu, Jesse Michael Han, Kunhao Zheng, et al.*2022 · 133 citations*](https://md.research.elamur.ai/free/en/papers/polu-2022-formal-mathematics-statement-curriculum-learning/) 4. [**Learning to Represent Programs with Graphs arXiv:1711.00740**Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi*2017 · 127 citations*](https://md.research.elamur.ai/free/en/papers/allamanis-2017-learning-represent-programs-graphs/) 5. [**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/) 6. [**Learning to Prove Theorems via Interacting with Proof Assistants arXiv:1905.09381**Kaiyu Yang, Jia Deng*2019 · 90 citations*](https://md.research.elamur.ai/free/en/papers/yang-2019-learning-prove-theorems-interacting-proof/) 7. [**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/) 8. [**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/) 9. [**Premise Order Matters in Reasoning with Large Language Models arXiv:2402.08939**Xinyun Chen, Ryan A. Chi, Xuezhi Wang, et al.*2024 · 58 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-premise-order-matters-reasoning-large/) 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. [**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/) 12. [**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/) 13. [**LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning arXiv:2101.06223**Yuhuai Wu, Markus Rabe, Wenda Li, et al.*2021 · 39 citations*](https://md.research.elamur.ai/free/en/papers/wu-2021-lime-learning-inductive-bias-primitives/) 14. [**Learning to Reason in Large Theories without Imitation arXiv:1905.10501**Kshitij Bansal, Christian Szegedy, Markus N. Rabe, et al.*2019 · 36 citations*](https://md.research.elamur.ai/free/en/papers/bansal-2019-learning-reason-large-theories-without/) 15. [**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/) 16. **[Graph Representations for Higher-Order Logic and Theorem Proving](https://md.research.elamur.ai/free/en/papers/paliwal-2019-graph-representations-higher-order-logic/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Aditya Paliwal, Sarah Loos, Markus Rabe, et al.*2019 · 33 citations* 17. **[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* 18. [**TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning arXiv:2102.09756**Minchao Wu, Michael Norrish, Christian Walder, et al.*2021 · 24 citations*](https://md.research.elamur.ai/free/en/papers/wu-2021-tacticzero-learning-prove-theorems-scratch/) 19. [**Solving Formal Math Problems by Decomposition and Iterative Reflection arXiv:2507.15225**Yichi Zhou, Jianqiu Zhao, Yongxin Zhang, et al.*2025 · 16 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2025-solving-formal-math-problems-decomposition/) 20. [**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/) 21. [**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/) 22. **[MPS-Prover: Advancing Stepwise Theorem Proving by Multi-Perspective Search and Data Curation](https://md.research.elamur.ai/free/en/papers/liang-2025-mps-prover-advancing-stepwise-theorem/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhenwen Liang, Linfeng Song, Yang Li, et al.*2025 · 4 citations* 23. **[Neural Wani: Toward Accelerating the Automated Theorem Prover wani for Dependent Type Theory](https://md.research.elamur.ai/free/en/papers/miyagawa-2026-neural-wani-accelerating-automated-theorem/) [Workshop on the Bridges and Gaps between Formal and Computational Linguistics](https://md.research.elamur.ai/free/en/venues/brigap/)**Nanako Miyagawa, Hinari Daido, Daisuke Bekki*2026* 11 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/wang-2017-premise-selection-theorem-proving-deep/) — Premise Selection for Theorem Proving by Deep Graph Embedding