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 Deepak Pathak'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 Deepak Pathak with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deepak Pathak more than expected).
This network shows the impact of papers produced by Deepak Pathak. 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 Deepak Pathak. The network helps show where Deepak Pathak may publish in the future.
Co-authorship network of co-authors of Deepak Pathak
This figure shows the co-authorship network connecting the top 25 collaborators of Deepak Pathak.
A scholar is included among the top collaborators of Deepak Pathak 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 Deepak Pathak. Deepak Pathak is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Huang, Wenlong, Igor Mordatch, & Deepak Pathak. (2020). One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control. International Conference on Machine Learning. 4455–4464.3 indexed citations
Emmons, Scott, Ajay N. Jain, Michael Laskin, et al.. (2020). Sparse Graphical Memory for Robust Planning. arXiv (Cornell University). 33. 5251–5262.2 indexed citations
13.
Collins, Jasmine, et al.. (2020). Exploring Exploration: Comparing Children with Agents in Unified Exploration Environments.. Cognitive Science.1 indexed citations
14.
Pathak, Deepak, et al.. (2019). Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity. Neural Information Processing Systems. 32. 2292–2302.7 indexed citations
15.
Azadi, Samaneh, et al.. (2019). Compositional GAN (Extended Abstract): Learning Image-Conditional Binary Composition. International Conference on Learning Representations.1 indexed citations
16.
Dubey, Rachit, et al.. (2018). Investigating Human Priors for Playing Video Games. International Conference on Machine Learning. 1348–1356.5 indexed citations
17.
Zhu, Jun-Yan, Richard Zhang, Deepak Pathak, et al.. (2017). Multimodal Image-to-Image Translation by Enforcing Bi-Cycle Consistency. Neural Information Processing Systems. 465–476.20 indexed citations
18.
Zhu, Jun, Richard Zhang, Deepak Pathak, et al.. (2017). Toward Multimodal Image-to-Image Translation. arXiv (Cornell University). 30. 465–476.203 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.