--- title: "Who cites Can Neural Networks Understand Logical Entailment?" description: "34 papers of the Elamur Research library cite «Can Neural Networks Understand Logical Entailment?», found in their bibliographies; the most…" canonical: "https://research.elamur.ai/free/en/papers/evans-2018-can-neural-networks-understand-logical/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/evans-2018-can-neural-networks-understand-logical/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Can Neural Networks Understand Logical Entailment?](https://md.research.elamur.ai/free/en/papers/evans-2018-can-neural-networks-understand-logical/) / cited by # Who cites «Can Neural Networks Understand Logical Entailment?» 34 papers of our library cite it (of the 149,439 whose bibliographies we read) · 17 with a page here, the most cited first · [← back to «Can Neural Networks Understand Logical Entailment?»](https://md.research.elamur.ai/free/en/papers/evans-2018-can-neural-networks-understand-logical/) 1. [**Relational inductive biases, deep learning, and graph networks arXiv:1806.01261**Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, et al.*2018 · 2,335 citations*](https://md.research.elamur.ai/free/en/papers/battaglia-2018-relational-inductive-biases-deep-learning/) 2. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,756 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/) 3. [**Analysing Mathematical Reasoning Abilities of Neural Models arXiv:1904.01557**David Saxton, Edward Grefenstette, Felix Hill, et al.*2019 · 288 citations*](https://md.research.elamur.ai/free/en/papers/saxton-2019-analysing-mathematical-reasoning-abilities-neural/) 4. [**Learning a SAT Solver from Single-Bit Supervision arXiv:1802.03685**Daniel Selsam, Matthew Lamm, Benedikt Bünz, et al.*2018 · 232 citations*](https://md.research.elamur.ai/free/en/papers/selsam-2018-learning-sat-solver-single-bit/) 5. **[The Importance of Being Recurrent for Modeling Hierarchical Structure](https://md.research.elamur.ai/free/en/papers/tran-2018-importance-being-recurrent-modeling-hierarchical/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ke Tran, Arianna Bisazza, Christof Monz*2018 · 228 citations* 6. **[Analyzing Compositionality-Sensitivity of NLI Models](https://md.research.elamur.ai/free/en/papers/nie-2018-analyzing-compositionality-sensitivity-nli-models/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Yixin Nie, Yicheng Wang, Mohit Bansal*2018 · 121 citations* 7. **[Posing Fair Generalization Tasks for Natural Language Inference](https://md.research.elamur.ai/free/en/papers/geiger-2019-posing-fair-generalization-tasks-natural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Atticus Geiger, Ignacio Cases, Lauri Karttunen, et al.*2019 · 61 citations* 8. [**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/) 9. [**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/) 10. **[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* 11. [**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/) 12. [**Learning advanced mathematical computations from examples arXiv:2006.06462**François Charton, Amaury Hayat, Guillaume Lample*2020 · 18 citations*](https://md.research.elamur.ai/free/en/papers/charton-2020-learning-advanced-mathematical-computations-examples/) 13. [**Adversarial Analysis of Natural Language Inference Systems arXiv:1912.03441**Tiffany Chien, Jugal Kalita*2019 · 15 citations*](https://md.research.elamur.ai/free/en/papers/chien-2019-adversarial-analysis-natural-language-inference/) 14. **[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* 15. [**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/) 16. [**Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement Learning arXiv:1807.08058**Gil Lederman, Markus N. Rabe, Edward A. Lee, et al.*2018 · 12 citations*](https://md.research.elamur.ai/free/en/papers/lederman-2018-learning-heuristics-quantified-boolean-formulas/) 17. **[NL2Logic: AST-Guided Translation of Natural Language into First-Order Logic with Large Language Models](https://md.research.elamur.ai/free/en/papers/putra-2026-nl2logic-ast-guided-translation-natural/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Rizky Ramadhana Putra, Raihan Sultan Pasha Basuki, Yutong Cheng, et al.*2026 · 3 citations* 17 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/evans-2018-can-neural-networks-understand-logical/) — Can Neural Networks Understand Logical Entailment?