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.
This map shows the geographic impact of Sylvain Gelly'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 Sylvain Gelly with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sylvain Gelly more than expected).
This network shows the impact of papers produced by Sylvain Gelly. 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 Sylvain Gelly. The network helps show where Sylvain Gelly may publish in the future.
Co-authorship network of co-authors of Sylvain Gelly
This figure shows the co-authorship network connecting the top 25 collaborators of Sylvain Gelly.
A scholar is included among the top collaborators of Sylvain Gelly 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 Sylvain Gelly. Sylvain Gelly is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Dosovitskiy, Alexey, Lucas Beyer, Alexander Kolesnikov, et al.. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. International Conference on Learning Representations.143 indexed citations
2.
Andrychowicz, Marcin, Anton Raichuk, Piotr Stańczyk, et al.. (2021). What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study. International Conference on Learning Representations.26 indexed citations
3.
Dumoulin, Vincent, Neil Houlsby, Utku Evci, et al.. (2021). A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches. Neural Information Processing Systems.2 indexed citations
4.
Alabdulmohsin, Ibrahim, et al.. (2020). What Do Neural Networks Learn When Trained With Random Labels. Neural Information Processing Systems. 33. 19693–19704.1 indexed citations
5.
Tschannen, Michael, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, & Mario Lučić. (2020). On Mutual Information Maximization for Representation Learning. arXiv (Cornell University).15 indexed citations
6.
Djolonga, Josip, Mario Lučić, Marco Cuturi, et al.. (2019). Evaluating Generative Models using Divergence Frontiers. arXiv (Cornell University).1 indexed citations
7.
Kolesnikov, Alexander, Lucas Beyer, Xiaohua Zhai, et al.. (2019). Large Scale Learning of General Visual Representations for Transfer.. arXiv (Cornell University).26 indexed citations
8.
Riquelme, Carlos, Hugo Penedones, Damien Vincent, et al.. (2019). Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates. arXiv (Cornell University). 32. 11872–11882.3 indexed citations
9.
Unterthiner, Thomas, Sjoerd van Steenkiste, Karol Kurach, et al.. (2019). FVD: A new Metric for Video Generation. International Conference on Learning Representations.27 indexed citations
10.
Lučić, Mario, Michael Tschannen, Marvin Ritter, et al.. (2019). High-Fidelity Image Generation With Fewer Labels. International Conference on Machine Learning. 4183–4192.16 indexed citations
11.
Zhai, Xiaohua, Joan Puigcerver, Alexander Kolesnikov, et al.. (2019). The Visual Task Adaptation Benchmark. arXiv (Cornell University).22 indexed citations
12.
Chen, Ting, Mario Lučić, Neil Houlsby, & Sylvain Gelly. (2018). On Self Modulation for Generative Adversarial Networks. arXiv (Cornell University).14 indexed citations
Gelly, Sylvain & David Silver. (2008). Achieving master level play in 9×9 computer go. UCL Discovery (University College London). 1537–1540.70 indexed citations
17.
Gelly, Sylvain, et al.. (2008). On the Parallelization of Monte-Carlo planning. SPIRE - Sciences Po Institutional REpository.20 indexed citations
18.
Gelly, Sylvain, et al.. (2006). Comparison-based algorithms: worst-case optimality, optimality w.r.t a bayesian prior, the intraclass-variance minimization in EDA, and implementations with billiards. SPIRE - Sciences Po Institutional REpository.1 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.