Karthik Narasimhan

41 papers receiving 1.1k citations

Hit Papers

Hierarchical deep reinforcement learning: integrating tem...20162026201920222016100200300

Peers

Karthik Narasimhan
Comparison fields: 5 of 112
  • Artificial Intelligence 844
  • Computer Vision and Pattern Recognition 259
  • Control and Systems Engineering 91
  • Information Systems 79
  • Electrical and Electronic Engineering 75
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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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 1
2 2
3 1
4 3
5 80
6 20
7 19
8
SILG: The Multi-domain Symbolic Interactive Language Grounding Benchmark
4
9
Accelerating Safe Reinforcement Learning with Constraint-mismatched Baseline Policies
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10 27
11 32
12
Multimodal Graph Networks for Compositional Generalization in Visual Question Answering
24
13
A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation
16
14
Task-agnostic dynamics priors for deep reinforcement learning
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15
Deep Transfer in Reinforcement Learning by Language Grounding.
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16
Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivationbreakdown →
318
17 41
18 76
19
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes
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20 25

About Karthik Narasimhan

Karthik Narasimhan is a scholar working on Artificial Intelligence, Health Informatics and Software, having authored 45 papers that have together received 1.1k indexed citations. Recurring topics across this work include Topic Modeling (20 papers), Natural Language Processing Techniques (12 papers) and Reinforcement Learning in Robotics (8 papers). The work is most often cited by research in Artificial Intelligence (844 citations), Computer Vision and Pattern Recognition (259 citations) and Health Informatics (11 citations). Karthik Narasimhan has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include Regina Barzilay, Ardavan Saeedi, Joshua B. Tenenbaum, Tejas D. Kulkarni, Tejas Kulkarni, Adam Yala, Tommi Jaakkola, Ashwin Kalyan, Tanmay Rajpurohit and Shunyu Yao. Their work appears in journals such as Journal of Neurophysiology, ACM Computing Surveys and Applied Sciences.

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.

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