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.
Reading Text in the Wild with Convolutional Neural Networks
2015751 citationsMax Jaderberg, Karen Simonyan et al.International Journal of Computer Visionprofile →
Speeding up Convolutional Neural Networks with Low Rank Expansions
2014735 citationsMax Jaderberg, Andrea Vedaldi et al.profile →
Human-level performance in 3D multiplayer games with population-based reinforcement learning
2019349 citationsMax Jaderberg, Wojciech Marian Czarnecki et al.Scienceprofile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
citations ·
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This map shows the geographic impact of Max Jaderberg'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 Max Jaderberg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Max Jaderberg more than expected).
This network shows the impact of papers produced by Max Jaderberg. 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 Max Jaderberg. The network helps show where Max Jaderberg may publish in the future.
Co-authorship network of co-authors of Max Jaderberg
This figure shows the co-authorship network connecting the top 25 collaborators of Max Jaderberg.
A scholar is included among the top collaborators of Max Jaderberg 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 Max Jaderberg. Max Jaderberg is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
14 of 14 papers shown
1.
Parisotto, Emilio, Francis Song, Jack W. Rae, et al.. (2020). Stabilizing Transformers for Reinforcement Learning. International Conference on Machine Learning. 1. 7487–7498.9 indexed citations
2.
Jaderberg, Max, Wojciech Marian Czarnecki, Iain Dunning, et al.. (2019). Human-level performance in 3D multiplayer games with population-based reinforcement learning. Science. 364(6443). 859–865.349 indexed citations breakdown →
3.
Balduzzi, David, Marta Garnelo, Yoram Bachrach, et al.. (2019). Open-ended learning in symmetric zero-sum games. UCL Discovery (University College London). 434–443.2 indexed citations
Czarnecki, Wojciech Marian, Simon Osindero, Max Jaderberg, Grzegorz Świrszcz, & Razvan Pascanu. (2017). Sobolev Training for Neural Networks. Neural Information Processing Systems. 30. 4278–4287.28 indexed citations
7.
Czarnecki, Wojciech Marian, Grzegorz Świrszcz, Max Jaderberg, et al.. (2017). Understanding Synthetic Gradients and Decoupled Neural Interfaces. International Conference on Machine Learning. 904–912.6 indexed citations
Fernando, Chrisantha, Dylan Banarse, Malcolm Reynolds, et al.. (2016). Convolution by Evolution. 109–116.44 indexed citations
11.
Jaderberg, Max, Karen Simonyan, Andrea Vedaldi, & Andrew Zisserman. (2015). Deep Structured Output Learning for Unconstrained Text Recognition. Oxford University Research Archive (ORA) (University of Oxford).98 indexed citations
12.
Jaderberg, Max, Karen Simonyan, Andrea Vedaldi, & Andrew Zisserman. (2015). Reading Text in the Wild with Convolutional Neural Networks. International Journal of Computer Vision. 116(1). 1–20.751 indexed citations breakdown →
13.
Jaderberg, Max, Andrea Vedaldi, & Andrew Zisserman. (2014). Speeding up Convolutional Neural Networks with Low Rank Expansions. 88.1–88.13.735 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.