Siddhant M. Jayakumar
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Computational Mathematics
- Co-authors
- Razvan PascanuSimon OsinderoJonathan SchwarzWojciech Marian CzarneckiYee Whye TehJack W. RaeTim HarleyJacob Menick
- Topics
- Multimodal Machine Learning Applications (2 papers)Neural Networks and Applications (2 papers)Reinforcement Learning in Robotics (2 papers)
- Journals
- arXiv (Cornell University)International Conference on Machine LearningInternational Conference on Learning Representations
- Partner nations
- United StatesUnited KingdomPoland
In The Last Decade
Siddhant M. Jayakumar
4 papers receiving 35 citations
Peers
Comparison fields: 5 of 18
- Artificial Intelligence 26
- Computer Vision and Pattern Recognition 12
- Control and Systems Engineering 4
- Electrical and Electronic Engineering 4
- Computational Mathematics 2
Countries citing papers authored by Siddhant M. Jayakumar
This map shows the geographic impact of Siddhant M. Jayakumar'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 Siddhant M. Jayakumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Siddhant M. Jayakumar more than expected).
Fields of papers citing papers by Siddhant M. Jayakumar
This network shows the impact of papers produced by Siddhant M. Jayakumar. 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 Siddhant M. Jayakumar. The network helps show where Siddhant M. Jayakumar may publish in the future.
Co-authorship network of co-authors of Siddhant M. Jayakumar
This figure shows the co-authorship network connecting the top 25 collaborators of Siddhant M. Jayakumar. A scholar is included among the top collaborators of Siddhant M. Jayakumar 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 Siddhant M. Jayakumar. Siddhant M. Jayakumar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | Multiplicative Interactions and Where to Find Them | 20 |
| 3 | Stabilizing Transformers for Reinforcement Learning | 9 |
| 4 | 4 |
About Siddhant M. Jayakumar
Siddhant M. Jayakumar is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Infectious Diseases, having authored 4 papers that have together received 35 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (2 papers), Neural Networks and Applications (2 papers) and Reinforcement Learning in Robotics (2 papers). The work is most often cited by research in Computational Mathematics (2 citations), Artificial Intelligence (26 citations) and Computer Vision and Pattern Recognition (12 citations). Siddhant M. Jayakumar has collaborated with scholars based in United States, United Kingdom and Poland. Frequent co-authors include Razvan Pascanu, Simon Osindero, Jonathan Schwarz, Wojciech Marian Czarnecki, Yee Whye Teh, Jack W. Rae, Tim Harley, Jacob Menick, Seb Noury and Aidan Clark. Their work appears in journals such as arXiv (Cornell University), International Conference on Machine Learning and International Conference on Learning Representations.
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.