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.
Human-level control through deep reinforcement learning
201517.2k citationsVolodymyr Mnih, Koray Kavukcuoglu et al.Natureprofile →
An Introduction to Deep Reinforcement Learning
2018811 citationsMarc G. Bellemare et al.profile →
An Introduction to Deep Reinforcement Learning
2018343 citationsMarc G. Bellemare et al.profile →
Distributional Reinforcement Learning With Quantile Regression
2018294 citationsWill Dabney, Mark Rowland et al.profile →
Countries citing papers authored by Marc G. Bellemare
Since
Specialization
Citations
This map shows the geographic impact of Marc G. Bellemare'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 Marc G. Bellemare with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marc G. Bellemare more than expected).
Fields of papers citing papers by Marc G. Bellemare
This network shows the impact of papers produced by Marc G. Bellemare. 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 Marc G. Bellemare. The network helps show where Marc G. Bellemare may publish in the future.
Co-authorship network of co-authors of Marc G. Bellemare
This figure shows the co-authorship network connecting the top 25 collaborators of Marc G. Bellemare.
A scholar is included among the top collaborators of Marc G. Bellemare 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 Marc G. Bellemare. Marc G. Bellemare is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Lan, Charline Le, Marc G. Bellemare, & Pablo Samuel Castro. (2021). Metrics and continuity in reinforcement learning. arXiv (Cornell University). 35(9). 8261–8269.3 indexed citations
Bellemare, Marc G., et al.. (2019). Temporally Extended Metrics for Markov Decision Processes.. National Conference on Artificial Intelligence.1 indexed citations
7.
Bellemare, Marc G., Will Dabney, Robert Dadashi, et al.. (2019). A Geometric Perspective on Optimal Representations for Reinforcement Learning. Neural Information Processing Systems. 32. 4358–4369.8 indexed citations
Rowland, Mark, Marc G. Bellemare, Will Dabney, Rémi Munos, & Yee Whye Teh. (2018). An Analysis of Categorical Distributional Reinforcement Learning. International Conference on Artificial Intelligence and Statistics. 29–37.4 indexed citations
Ostrovski, Georg, Marc G. Bellemare, Aäron van den Oord, & Rémi Munos. (2017). Count-based exploration with neural density models. International Conference on Machine Learning. 2721–2730.43 indexed citations
Harutyunyan, Anna, Marc G. Bellemare, Tom Stepleton, & Rémi Munos. (2016). Q($λ$) with Off-Policy Corrections. arXiv (Cornell University). 305–320.6 indexed citations
15.
Bellemare, Marc G.. (2015). Count-based frequency estimation with bounded memory. International Conference on Artificial Intelligence. 3337–3344.
16.
Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, et al.. (2015). Human-level control through deep reinforcement learning. Nature. 518(7540). 529–533.17153 indexed citations breakdown →
17.
Bellemare, Marc G., Joel Veness, & Erik Talvitie. (2014). Skip Context Tree Switching. International Conference on Machine Learning. 1458–1466.10 indexed citations
18.
Bellemare, Marc G., Joel Veness, & Michael Bowling. (2013). Bayesian Learning of Recursively Factored Environments. International Conference on Machine Learning. 1211–1219.10 indexed citations
19.
Bellemare, Marc G., Joel Veness, & Michael Bowling. (2012). Sketch-Based Linear Value Function Approximation. Neural Information Processing Systems. 25. 2213–2221.11 indexed citations
20.
Bellemare, Marc G. & Doina Precup. (2007). Context-driven predictions. International Joint Conference on Artificial Intelligence. 250–255.1 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.