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 →
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 →
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
20181.8k citationsDavid Silver, Thomas Hubert et al.Scienceprofile →
Citations per year, relative to Ioannis Antonoglou Ioannis Antonoglou (= 1×)
peers
Koray Kavukcuoglu
Countries citing papers authored by Ioannis Antonoglou
Since
Specialization
Citations
This map shows the geographic impact of Ioannis Antonoglou'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 Ioannis Antonoglou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ioannis Antonoglou more than expected).
Fields of papers citing papers by Ioannis Antonoglou
This network shows the impact of papers produced by Ioannis Antonoglou. 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 Ioannis Antonoglou. The network helps show where Ioannis Antonoglou may publish in the future.
Co-authorship network of co-authors of Ioannis Antonoglou
This figure shows the co-authorship network connecting the top 25 collaborators of Ioannis Antonoglou.
A scholar is included among the top collaborators of Ioannis Antonoglou 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 Ioannis Antonoglou. Ioannis Antonoglou is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
6 of 6 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.
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 →
3.
Silver, David, Julian Schrittwieser, Karen Simonyan, et al.. (2017). Mastering the game of Go without human knowledge. Nature. 550(7676). 354–359.5038 indexed citations breakdown →
4.
Belilovsky, Eugene, et al.. (2016). A Test of Relative Similarity For Model Selection in Generative Models. Lirias (KU Leuven).14 indexed citations
5.
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 →
6.
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 →
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.