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.
Countries citing papers authored by Karthik Narasimhan
Since
Specialization
Citations
This map shows the geographic impact of Karthik Narasimhan'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 Karthik Narasimhan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Karthik Narasimhan more than expected).
Fields of papers citing papers by Karthik Narasimhan
This network shows the impact of papers produced by Karthik Narasimhan. 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 Karthik Narasimhan. The network helps show where Karthik Narasimhan may publish in the future.
Co-authorship network of co-authors of Karthik Narasimhan
This figure shows the co-authorship network connecting the top 25 collaborators of Karthik Narasimhan.
A scholar is included among the top collaborators of Karthik Narasimhan 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 Karthik Narasimhan. Karthik Narasimhan is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zhong, Victor W., et al.. (2021). SILG: The Multi-domain Symbolic Interactive Language Grounding Benchmark. Neural Information Processing Systems. 34.4 indexed citations
8.
Rosca, Justinian, et al.. (2021). Accelerating Safe Reinforcement Learning with Constraint-mismatched Baseline Policies. International Conference on Machine Learning. 11795–11807.8 indexed citations
9.
Narasimhan, Karthik, et al.. (2020). Multimodal Graph Networks for Compositional Generalization in Visual Question Answering. Neural Information Processing Systems. 33. 3070–3081.24 indexed citations
Du, Yilun & Karthik Narasimhan. (2019). Task-agnostic dynamics priors for deep reinforcement learning. International Conference on Machine Learning. 1696–1705.
13.
Yang, Runzhe, Xingyuan Sun, & Karthik Narasimhan. (2019). A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation. Neural Information Processing Systems. 32. 14610–14621.16 indexed citations
14.
Narasimhan, Karthik, Regina Barzilay, & Tommi Jaakkola. (2017). Deep Transfer in Reinforcement Learning by Language Grounding.. arXiv (Cornell University).2 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.