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 James Martens'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 James Martens with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Martens more than expected).
This network shows the impact of papers produced by James Martens. 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 James Martens. The network helps show where James Martens may publish in the future.
Co-authorship network of co-authors of James Martens
This figure shows the co-authorship network connecting the top 25 collaborators of James Martens.
A scholar is included among the top collaborators of James Martens 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 James Martens. James Martens 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.
Martens, James. (2020). New Insights and Perspectives on the Natural Gradient Method. Journal of Machine Learning Research. 21(146). 1–76.76 indexed citations
2.
Zhang, Guodong, James Martens, & Roger Grosse. (2019). Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks. arXiv (Cornell University). 32. 8080–8091.5 indexed citations
3.
Qin, Chongli, James Martens, Sven Gowal, et al.. (2019). Adversarial Robustness through Local Linearization. Neural Information Processing Systems. 32. 13824–13833.27 indexed citations
4.
Balduzzi, David, Sébastien Racanière, James Martens, et al.. (2019). Differentiable Game Mechanics. Journal of Machine Learning Research. 20(84). 1–40.6 indexed citations
5.
Balduzzi, David, Sébastien Racanière, James Martens, et al.. (2018). The Mechanics of n-Player Differentiable Games. UCL Discovery (University College London). 354–363.13 indexed citations
6.
Martens, James, et al.. (2018). Kronecker-factored Curvature Approximations for Recurrent Neural Networks. International Conference on Learning Representations.13 indexed citations
7.
Nado, Zachary, et al.. (2018). STOCHASTIC GRADIENT LANGEVIN DYNAMICS THAT EXPLOIT NEURAL NETWORK STRUCTURE. International Conference on Learning Representations.4 indexed citations
8.
Ba, Jimmy, Roger Grosse, & James Martens. (2017). Distributed Second-Order Optimization using Kronecker-Factored Approximations. International Conference on Learning Representations.24 indexed citations
9.
Martens, James & Roger Grosse. (2015). Optimizing Neural Networks with Kronecker-factored Approximate Curvature. International Conference on Machine Learning. 2408–2417.29 indexed citations
Martens, James, et al.. (2013). On the Representational Efficiency of Restricted Boltzmann Machines. neural information processing systems. 26. 2877–2885.11 indexed citations
12.
Martens, James, Arkadev Chattopadhyay, Toniann Pitassi, & Richard S. Zemel. (2013). On the Expressive Power of Restricted Boltzmann Machines.. Neural Information Processing Systems. 2877–2885.4 indexed citations
13.
Martens, James & Ilya Sutskever. (2011). Learning Recurrent Neural Networks with Hessian-Free Optimization. International Conference on Machine Learning. 1033–1040.300 indexed citations
Martens, James. (2010). Learning the Linear Dynamical System with ASOS. International Conference on Machine Learning. 743–750.6 indexed citations
18.
Martens, James & Ilya Sutskever. (2010). Parallelizable Sampling of Markov Random Fields. International Conference on Artificial Intelligence and Statistics. 517–524.8 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.