---
title: "Who cites The Use of Deep Learning for — 7 citing papers"
description: "7 papers of the Elamur Research library cite «The Use of Deep Learning for Symbolic Integration: A Review of (Lample and Charton, 2019)»…"
canonical: "https://research.elamur.ai/free/en/papers/davis-2019-use-deep-learning-symbolic-integration/cited-by/"
---
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# Who cites «The Use of Deep Learning for Symbolic Integration: A Review of (Lample and Charton, 2019)»

7 papers of our library cite it (of the 149,439 whose bibliographies we read) · 4 with a page here, the most cited first · [← back to «The Use of Deep Learning for Symbolic Integration: A Review of (Lample and Charton, 2019)»](https://md.research.elamur.ai/free/en/papers/davis-2019-use-deep-learning-symbolic-integration/)

1. [**The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence arXiv:2002.06177**Gary Marcus*2020 · 332 citations*](https://md.research.elamur.ai/free/en/papers/marcus-2020-next-decade-ai-four/)
2. [**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/)
3. [**Neurosymbolic AI: The 3rd Wave arXiv:2012.05876**Artur d'Avila Garcez, Luis C. Lamb*2020 · 20 citations*](https://md.research.elamur.ai/free/en/papers/garcez-2020-neurosymbolic-ai-3rd-wave/)
4. **[A Symbolic Framework for Evaluating Mathematical Reasoning and Generalisation with Transformers](https://md.research.elamur.ai/free/en/papers/meadows-2024-symbolic-framework-evaluating-mathematical-reasoning/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jordan Meadows, Marco Valentino, Damien Teney, et al.*2024 · 14 citations*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/davis-2019-use-deep-learning-symbolic-integration/) — The Use of Deep Learning for Symbolic Integration: A Review of (Lample and Charton, 2019)