Hubert Soyer

4.5k total citations · 1 hit paper
8 papers, 627 citations indexed

About

Hubert Soyer is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Hubert Soyer has authored 8 papers receiving a total of 627 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 2 papers in Molecular Biology and 1 paper in Computer Vision and Pattern Recognition. Recurrent topics in Hubert Soyer's work include Topic Modeling (3 papers), Natural Language Processing Techniques (3 papers) and Reinforcement Learning in Robotics (3 papers). Hubert Soyer is often cited by papers focused on Topic Modeling (3 papers), Natural Language Processing Techniques (3 papers) and Reinforcement Learning in Robotics (3 papers). Hubert Soyer collaborates with scholars based in United Kingdom, Italy and Japan. Hubert Soyer's co-authors include Dharshan Kumaran, Dhruva Tirumala, Joel Z. Leibo, Zeb Kurth‐Nelson, Demis Hassabis, Matthew Botvinick, Jane X. Wang, Matteo Hessel, Lasse Espeholt and Simon Schmitt and has published in prestigious journals such as Nature Neuroscience, arXiv (Cornell University) and Proceedings of the AAAI Conference on Artificial Intelligence.

In The Last Decade

Hubert Soyer

8 papers receiving 608 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Prefrontal cortex as a meta-reinforcement learning system

2018 317 citations Jane X. Wang, Zeb Kurth‐Nelson et al. Nature Neuroscience profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Hubert Soyer United Kingdom	5	296	243	130	54	53	8	627
Katsunori Shimohara Japan	17	346 1.2×	212 0.9×	91 0.7×	89 1.6×	65 1.2×	194	951
Andrea Soltoggio United Kingdom	15	267 0.9×	128 0.5×	150 1.2×	47 0.9×	98 1.8×	43	694
Stefano Nichele Norway	10	218 0.7×	324 1.3×	214 1.6×	24 0.4×	101 1.9×	49	889
Matthew Luciw Switzerland	13	207 0.7×	256 1.1×	94 0.7×	73 1.4×	86 1.6×	37	493
Luke Marris United Kingdom	3	398 1.3×	254 1.0×	83 0.6×	52 1.0×	206 3.9×	3	818
Wanli Ma Australia	16	263 0.9×	191 0.8×	134 1.0×	26 0.5×	55 1.0×	105	842
Paolo Gaudiano United States	13	125 0.4×	206 0.8×	150 1.2×	89 1.6×	36 0.7×	38	541
Siddharth Srivastava United States	16	471 1.6×	166 0.7×	332 2.6×	191 3.5×	30 0.6×	77	967
Olivier Sigaud France	17	403 1.4×	210 0.9×	81 0.6×	265 4.9×	34 0.6×	50	906

Countries citing papers authored by Hubert Soyer

Since Specialization

Citations

This map shows the geographic impact of Hubert Soyer's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hubert Soyer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hubert Soyer more than expected).

Fields of papers citing papers by Hubert Soyer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hubert Soyer. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hubert Soyer. The network helps show where Hubert Soyer may publish in the future.

Co-authorship network of co-authors of Hubert Soyer

This figure shows the co-authorship network connecting the top 25 collaborators of Hubert Soyer. A scholar is included among the top collaborators of Hubert Soyer based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Hubert Soyer. Hubert Soyer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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8 of 8 papers shown

1.

Song, Hao, Abbas Abdolmaleki, Jost Tobias Springenberg, et al.. (2020). V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control. arXiv (Cornell University). 3 indexed citations

2.

Hessel, Matteo, Hubert Soyer, Lasse Espeholt, et al.. (2019). Multi-Task Deep Reinforcement Learning with PopArt. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 3796–3803. 106 indexed citations

3.

Wang, Jane X., Zeb Kurth‐Nelson, Dharshan Kumaran, et al.. (2018). Prefrontal cortex as a meta-reinforcement learning system. Nature Neuroscience. 21(6). 860–868. 317 indexed citations breakdown →

4.

Ruderman, Avraham, Richard Everett, Hubert Soyer, et al.. (2018). Uncovering Surprising Behaviors in Reinforcement Learning via Worst-case Analysis. 6 indexed citations

5.

Mirowski, Piotr, Razvan Pascanu, Fabio Viola, et al.. (2016). Learning to Navigate in Complex Environments. arXiv (Cornell University). 173 indexed citations

6.

Soyer, Hubert, Goran Topić, Pontus Stenetorp, & Akiko Aizawa. (2015). CroVeWA: Crosslingual Vector-Based Writing Assistance. 91–95. 2 indexed citations

7.

Soyer, Hubert, et al.. (2014). Japanese to English Machine Translation using Preordering and Compositional Distributed Semantics. 55–63. 3 indexed citations

8.

Stenetorp, Pontus, Hubert Soyer, Sampo Pyysalo, Sophia Ananiadou, & Takashi Chikayama. (2012). Size (and Domain) Matters: Evaluating Semantic Word Space Representations for Biomedical Text. 17 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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