---
title: "Who cites Alvaro Sanchez-Gonzalez — 204 papers of our library"
description: "204 papers of the Elamur Research library cite the works of Alvaro Sanchez-Gonzalez, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/alvaro-sanchez-gonzalez/cited-by/"
---
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# Who cites Alvaro Sanchez-Gonzalez

208 citations of their works from 204 papers of our library (of the 149,439 whose bibliographies we read) · 28 with a page here, the most cited first · [← Alvaro Sanchez-Gonzalez](https://md.research.elamur.ai/free/en/authors/alvaro-sanchez-gonzalez/)

1. **[A Comprehensive Survey on Graph Neural Networks](https://md.research.elamur.ai/free/en/papers/wu-2019-comprehensive-survey-graph-neural-networks/) [IEEE Transactions on Neural Networks and Learning Systems](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-neural-networks-and-learning-systems/)**Zonghan Wu, Shirui Pan, Fengwen Chen, et al.*2019 · 10,312 citations*
2. **[SuperGlue: Learning Feature Matching with Graph Neural Networks](https://md.research.elamur.ai/free/en/papers/sarlin-2019-superglue-learning-feature-matching-graph/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, et al.*2019 · 2,921 citations*
3. [**Fast Graph Representation Learning with PyTorch Geometric arXiv:1903.02428**Matthias Fey, Jan Eric Lenssen*2019 · 1,440 citations*](https://md.research.elamur.ai/free/en/papers/fey-2019-fast-graph-representation-learning-pytorch/)
4. [**Discovering Symbolic Models from Deep Learning with Inductive Biases arXiv:2006.11287**Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, et al.*2020 · 272 citations*](https://md.research.elamur.ai/free/en/papers/cranmer-2020-discovering-symbolic-models-deep/)
5. [**Scalable agent alignment via reward modeling: a research direction arXiv:1811.07871**Jan Leike, David Krueger, Tom Everitt, et al.*2018 · 227 citations*](https://md.research.elamur.ai/free/en/papers/leike-2018-scalable-agent-alignment-reward-modeling/)
6. [**Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl arXiv:2305.01582**Miles Cranmer (Princeton University, Flatiron Institute)*2023 · 216 citations*](https://md.research.elamur.ai/free/en/papers/university-2023-interpretable-machine-learning-science/)
7. **[Multimodal Transformer for Multimodal Machine Translation](https://md.research.elamur.ai/free/en/papers/yao-2020-multimodal-transformer-multimodal-machine-translation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shaowei Yao, Xiaojun Wan*2020 · 200 citations*
8. [**Neural Networks and the Chomsky Hierarchy arXiv:2207.02098**Grégoire Delétang, Anian Ruoss, Jordi Grau-Moya, et al.*2022 · 112 citations*](https://md.research.elamur.ai/free/en/papers/deletang-2022-neural-networks-chomsky-hierarchy/)
9. **[AMR-To-Text Generation with Graph Transformer](https://md.research.elamur.ai/free/en/papers/wang-2020-amr-text-generation-graph-transformer/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Tianming Wang, Xiaojun Wan, Hanqi Jin*2020 · 108 citations*
10. **[Graph-based Dependency Parsing with Graph Neural Networks](https://md.research.elamur.ai/free/en/papers/ji-2019-graph-based-dependency-parsing-graph/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tao Ji, Yuanbin Wu, Man Lan*2019 · 94 citations*
11. **[Improving Compositional Generalization with Latent Structure and Data Augmentation](https://md.research.elamur.ai/free/en/papers/qiu-2022-improving-compositional-generalization-latent-structure/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Linlu Qiu, Peter Shaw, Panupong Pasupat, et al.*2022 · 76 citations*
12. [**StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts**Zhengxiang Shi, Qiang Zhang, Aldo Lipani*2022 · 71 citations*](https://md.research.elamur.ai/free/en/papers/shi-2022-stepgame-new-benchmark-robust-multi/)
13. [**CoPhy: Counterfactual Learning of Physical Dynamics arXiv:1909.12000**Fabien Baradel, Natalia Neverova, Julien Mille, et al.*2019 · 40 citations*](https://md.research.elamur.ai/free/en/papers/baradel-2019-cophy-counterfactual-learning-physical-dynamics/)
14. **[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*
15. [**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/)
16. [**Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models arXiv:2005.14709**Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro*2020 · 24 citations*](https://md.research.elamur.ai/free/en/papers/schlegel-2020-leaderboards-survey-methods-revealing-weaknesses/)
17. **[Symbolic Regression with a Learned Concept Library](https://md.research.elamur.ai/free/en/papers/grayeli-2024-symbolic-regression-learned-concept-library/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Arya Grayeli, Atharva Sehgal, Omar Costilla-Reyes, et al.*2024 · 23 citations*
18. [**Physion: Evaluating Physical Prediction from Vision in Humans and Machines arXiv:2106.08261**Daniel M. Bear, Elias Wang, Damian Mrowca, et al.*2021 · 23 citations*](https://md.research.elamur.ai/free/en/papers/bear-2021-physion-evaluating-physical-prediction-vision/)
19. **[Understanding Transformer Reasoning Capabilities via Graph Algorithms](https://md.research.elamur.ai/free/en/papers/sanford-2024-understanding-transformer-reasoning-capabilities-graph/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Clayton Sanford, Bahare Fatemi, Ethan Hall, et al.*2024 · 21 citations*
20. **[Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems](https://md.research.elamur.ai/free/en/papers/kramer-2023-automated-scientific-discovery-equation/) [Machine Learning](https://md.research.elamur.ai/free/en/venues/machine-learning/)**Stefan Kramer, Mattia Cerrato, Jannis Brugger, et al.*2023 · 13 citations*
21. [**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/)
22. [**SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training arXiv:2310.02227**Kazem Meidani, Parshin Shojaee, Chandan K. Reddy, et al.*2023 · 8 citations*](https://md.research.elamur.ai/free/en/papers/meidani-2023-snip-bridging-mathematical-symbolic-numeric/)
23. [**Neurosymbolic Programming for Science arXiv:2210.05050**Jennifer J. Sun, Megan Tjandrasuwita, Atharva Sehgal, et al.*2022 · 6 citations*](https://md.research.elamur.ai/free/en/papers/sun-2022-neurosymbolic-programming-science/)
24. [**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/)
25. **[Graph-Based Alternatives to LLMs for Human Simulation](https://md.research.elamur.ai/free/en/papers/suh-2026-graph-based-alternatives-llms-human/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Joseph Suh, Suhong Moon, Serina Chang*2026*
26. **[MuSe: Multi-Stage Graph Reasoning via Vision-Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-muse-multi-stage-graph-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guanyu Wang, Xu Chu, Zhijie Tan, et al.*2026*
27. **[Methods for Constraint Solving Problems Based on Learn to Reason Model: A Review (基于学习-推理的约束求解方法研究进展)](https://md.research.elamur.ai/free/en/papers/zou-2023-methods-constraint-solving/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Yue Zou (邹悦), Jiayang Lai (赖家洋), Yonggang Zhang (张永刚)*2023*
28. **[Overview on Knowledge Graph Embedding Technology Research (知识图谱嵌入技术研究综述)](https://md.research.elamur.ai/free/en/papers/zhang-2021-overview-knowledge-graph/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Tiancheng Zhang (张天成), Xue Tian (田雪), Xianghui Sun (孙相会), et al.*2021*

176 more citing papers of our library have no page here yet.

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