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 citationsKoray Kavukcuoglu, David Silver et al.Natureprofile →
Mastering the game of Go with deep neural networks and tree search
20168.8k citationsDavid Silver, Aja Huang et al.Natureprofile →
Mastering the game of Go without human knowledge
20175.0k citationsDavid Silver, Julian Schrittwieser et al.Natureprofile →
This map shows the geographic impact of David Silver'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 David Silver with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Silver more than expected).
This network shows the impact of papers produced by David Silver. 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 David Silver. The network helps show where David Silver may publish in the future.
Co-authorship network of co-authors of David Silver
This figure shows the co-authorship network connecting the top 25 collaborators of David Silver.
A scholar is included among the top collaborators of David Silver 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 David Silver. David Silver 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.
Hubert, Thomas, Julian Schrittwieser, Ioannis Antonoglou, et al.. (2021). Learning and Planning in Complex Action Spaces. International Conference on Machine Learning. 4476–4486.7 indexed citations
2.
Guez, Arthur, Fabio Viola, Théophane Weber, et al.. (2020). Value-driven Hindsight Modelling. Neural Information Processing Systems. 33. 12499–12509.1 indexed citations
3.
Oh, Junhyuk, Matteo Hessel, Wojciech Marian Czarnecki, et al.. (2020). Discovering Reinforcement Learning Algorithms. Neural Information Processing Systems. 33. 1060–1070.1 indexed citations
4.
Zahavy, Tom, Zhongwen Xu, Vivek Veeriah, et al.. (2020). A Self-Tuning Actor-Critic Algorithm. Neural Information Processing Systems. 33. 20913–20924.2 indexed citations
5.
Xu, Zhongwen, Hado P. van Hasselt, Matteo Hessel, et al.. (2020). Meta-Gradient Reinforcement Learning with an Objective Discovered Online. Neural Information Processing Systems. 33. 15254–15264.2 indexed citations
6.
Horgan, Dan, John Quan, David Budden, et al.. (2018). Distributed Prioritized Experience Replay. International Conference on Learning Representations.56 indexed citations
7.
Silver, David, Thomas Hubert, Julian Schrittwieser, et al.. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science. 362(6419). 1140–1144.1827 indexed citations breakdown →
8.
Dabney, Will, Georg Ostrovski, David Silver, & Rémi Munos. (2018). Implicit Quantile Networks for Distributional Reinforcement Learning. arXiv (Cornell University). 1096–1105.19 indexed citations
9.
Silver, David, Hado van Hasselt, Matteo Hessel, et al.. (2017). The predictron: end-to-end learning and planning. International Conference on Machine Learning. 3191–3199.25 indexed citations
10.
Silver, David, Aja Huang, Chris J. Maddison, et al.. (2016). Mastering the game of Go with deep neural networks and tree search. Nature. 529(7587). 484–489.8793 indexed citations breakdown →
Guez, Arthur, Nicolas Heess, David Silver, & Peter Dayan. (2014). Bayes-Adaptive Simulation-based Search with Value Function Approximation. UCL Discovery (University College London). 27. 451–459.7 indexed citations
13.
Silver, David, Suman Jana, Dan Boneh, Eric Chen, & Collin Jackson. (2014). Password managers: attacks and defenses. USENIX Security Symposium. 449–464.55 indexed citations
14.
Silver, David, et al.. (2013). Concurrent Reinforcement Learning from Customer Interactions. UCL Discovery (University College London). 924–932.24 indexed citations
Heess, Nicolas, David Silver, & Yee Whye Teh. (2012). Actor-Critic Reinforcement Learning with Energy-Based Policies. UCL Discovery (University College London). 43–58.26 indexed citations
17.
Branavan, S. R. K., David Silver, & Regina Barzilay. (2011). Non-linear Monte-Carlo search in civilization II. International Joint Conference on Artificial Intelligence. 2404–2410.16 indexed citations
Bhatnagar, Shalabh, Doina Precup, David Silver, et al.. (2009). Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation. Neural Information Processing Systems. 22. 1204–1212.81 indexed citations
20.
Silver, David. (2005). Cooperative Pathfinding. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 1(1). 117–122.426 indexed citations breakdown →
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.