---
title: "Who cites Learning Continuous Semantic — 17 citing papers"
description: "17 papers of the Elamur Research library cite «Learning Continuous Semantic Representations of Symbolic Expressions», found in their…"
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---
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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»](https://md.research.elamur.ai/free/en/papers/allamanis-2016-learning-continuous-semantic-representations-symbolic/)

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. **[A Survey of Machine Learning for Big Code and Naturalness](https://md.research.elamur.ai/free/en/papers/allamanis-2017-survey-machine-learning-big-code/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, et al.*2017 · 167 citations*
5. [**Deep Learning for Symbolic Mathematics arXiv:1912.01412**Guillaume Lample, François Charton*2019 · 138 citations*](https://md.research.elamur.ai/free/en/papers/lample-2019-deep-learning-symbolic-mathematics/)
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. [**Can Neural Networks Understand Logical Entailment? arXiv:1802.08535**Richard Evans, David Saxton, David Amos, et al.*2018 · 44 citations*](https://md.research.elamur.ai/free/en/papers/evans-2018-can-neural-networks-understand-logical/)
8. **[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*
9. [**Deep Symbolic Regression for Recurrent Sequences arXiv:2201.04600**Stéphane d'Ascoli, Pierre-Alexandre Kamienny, Guillaume Lample, et al.*2022 · 27 citations*](https://md.research.elamur.ai/free/en/papers/ascoli-2022-deep-symbolic-regression-recurrent-sequences/)
10. [**Neuro-Symbolic Artificial Intelligence: Current Trends arXiv:2105.05330**Md Kamruzzaman Sarker, Lu Zhou, Aaron Eberhart, et al.*2021 · 12 citations*](https://md.research.elamur.ai/free/en/papers/sarker-2021-neuro-symbolic-artificial-intelligence/)
11. [**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/)

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

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