---
title: "Who cites Teaching Temporal Logics to Neural Networks"
description: "20 papers of the Elamur Research library cite «Teaching Temporal Logics to Neural Networks», found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/papers/hahn-2020-teaching-temporal-logics-neural-networks/cited-by/"
---
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# Who cites «Teaching Temporal Logics to Neural Networks»

20 papers of our library cite it (of the 149,439 whose bibliographies we read) · 10 with a page here, the most cited first · [← back to «Teaching Temporal Logics to Neural Networks»](https://md.research.elamur.ai/free/en/papers/hahn-2020-teaching-temporal-logics-neural-networks/)

1. [**Autoformalization with Large Language Models arXiv:2205.12615**Yuhuai Wu, Albert Q. Jiang, Wenda Li, et al.*2022 · 204 citations*](https://md.research.elamur.ai/free/en/papers/wu-2022-autoformalization-large-language-models/)
2. **[LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models](https://md.research.elamur.ai/free/en/papers/parmar-2024-logicbench-systematic-evaluation-logical-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mihir Parmar, Nisarg Patel, Neeraj Varshney, et al.*2024 · 93 citations*
3. [**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/)
4. [**Mathematical Reasoning via Self-supervised Skip-tree Training arXiv:2006.04757**Markus N. Rabe, Dennis Lee, Kshitij Bansal, et al.*2020 · 54 citations*](https://md.research.elamur.ai/free/en/papers/rabe-2020-mathematical-reasoning-self-supervised-skip/)
5. [**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/)
6. [**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/)
7. [**Linear algebra with transformers arXiv:2112.01898**François Charton*2021 · 31 citations*](https://md.research.elamur.ai/free/en/papers/charton-2021-linear-algebra-transformers/)
8. [**Length Generalization in Arithmetic Transformers arXiv:2306.15400**Samy Jelassi, Stéphane d'Ascoli, Carles Domingo-Enrich, et al.*2023 · 29 citations*](https://md.research.elamur.ai/free/en/papers/jelassi-2023-length-generalization-arithmetic-transformers/)
9. [**End-to-end symbolic regression with transformers arXiv:2204.10532**Pierre-Alexandre Kamienny, Stéphane d'Ascoli, Guillaume Lample, et al.*2022 · 27 citations*](https://md.research.elamur.ai/free/en/papers/kamienny-2022-end-end-symbolic-regression-transformers/)
10. [**From Statistical Relational to Neurosymbolic Artificial Intelligence: a Survey arXiv:2108.11451**Giuseppe Marra, Sebastijan Dumančić, Robin Manhaeve, et al.*2021 · 3 citations*](https://md.research.elamur.ai/free/en/papers/marra-2021-statistical-relational-neurosymbolic-artificial/)

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

- [← back to](https://md.research.elamur.ai/free/en/papers/hahn-2020-teaching-temporal-logics-neural-networks/) — Teaching Temporal Logics to Neural Networks