Shariq Iqbal
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- Reinforcement Learning in Robotics 3
- Domain Adaptation and Few-Shot Learning 2
- Machine Learning and Data Classification 2
- Artificial Intelligence in Games 2
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- Advanced Image and Video Retrieval Techniques 1
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- Neural dynamics and brain function 1
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- Software Engineering Research 2
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- Robotics and Automated Systems 1
- Co-authors
- Fei ShaSébastien M. R. ArnoldShimon WhitesonJohn PearsonMichael L. PlattBei PengE. N. JohnsonWendelin Böhmer
- Journals
- PLoS Computational Biology (1 paper)arXiv (Cornell University) (3 papers)Research Repository (Delft University of Technology) (1 paper)
- Partner nations
- United StatesUnited KingdomNetherlands
In The Last Decade
Shariq Iqbal
7 papers receiving 33 citations
Peers
Comparison fields: 5 of 22
- Human-Computer Interaction 5
- Artificial Intelligence 19
- Computer Vision and Pattern Recognition 10
- Cognitive Neuroscience 7
- Safety Research 2
Countries citing papers authored by Shariq Iqbal
This map shows the geographic impact of Shariq Iqbal'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 Shariq Iqbal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shariq Iqbal more than expected).
Fields of papers citing papers by Shariq Iqbal
This network shows the impact of papers produced by Shariq Iqbal. 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 Shariq Iqbal. The network helps show where Shariq Iqbal may publish in the future.
Co-authorship network
The 14 scholars most cited alongside Shariq Iqbal, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning | 2021 | 7 |
| 2 | When MAML Can Adapt Fast and How to Assist When It Cannot | 2021 | 5 |
| 3 | AI-QMIX: Attention and Imagination for Dynamic Multi-Agent Reinforcement Learning | 2020 | 6 |
| 4 | 2019 | 4 | |
| 5 | Decoupling Adaptation from Modeling with Meta-Optimizers for Meta Learning | 2019 | 3 |
| 6 | Directional Semantic Grasping of Real-World Objects: From Simulation to Reality. | 2019 | 3 |
| 7 | 2018 | 5 |
About Shariq Iqbal
Shariq Iqbal is a scholar working on Artificial Intelligence, Human-Computer Interaction and Computer Vision and Pattern Recognition, having authored 7 papers that have together received 33 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (3 papers), Software Engineering Research (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Machine Learning and Data Classification (2 papers), Artificial Intelligence in Games (2 papers), Robotics and Automated Systems (1 paper), Advanced Image and Video Retrieval Techniques (1 paper) and Neural dynamics and brain function (1 paper). The work is most often cited by research in Human-Computer Interaction (5 citations), Artificial Intelligence (19 citations) and Computer Vision and Pattern Recognition (10 citations). Shariq Iqbal has collaborated with scholars based in United States, United Kingdom and Netherlands. Frequent co-authors include Fei Sha, Sébastien M. R. Arnold, Shimon Whiteson, John Pearson, Michael L. Platt, Bei Peng, E. N. Johnson, Wendelin Böhmer, Bei Peng and C. Schroeder. Their work appears in journals such as PLoS Computational Biology, arXiv (Cornell University) and Research Repository (Delft University of Technology).
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.