I. Babuschkin
Impact in
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- Reinforcement Learning in Robotics
- Explainable Artificial Intelligence (XAI)
- Domain Adaptation and Few-Shot Learning
- Artificial Intelligence in Games
- Topic Modeling
- Anomaly Detection Techniques and Applications
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- Multimodal Machine Learning Applications
- Generative Adversarial Networks and Image Synthesis
Papers in
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- Adversarial Robustness in Machine Learning 1
- Neural Networks and Applications 1
- Computational Physics and Python Applications 1
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- Cell Image Analysis Techniques 2
- Co-authors
- Oriol Vinyals (2 shared papers)Karl Tuyls (1 shared paper)David Raposo (1 shared paper)Murray Shanahan (1 shared paper)Edward Lockhart (1 shared paper)Victoria Langston (1 shared paper)Razvan Pascanu (1 shared paper)Yujia Li (1 shared paper)
- Journals
- Journal of Physics Conference Series (1 paper)International Conference on Machine Learning (1 paper)International Conference on Learning Representations (1 paper)Zenodo (CERN European Organization for Nuclear Research) (2 papers)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesAustraliaCanada
In The Last Decade
I. Babuschkin
5 papers receiving 78 citations
Peers
Comparison fields: 5 of 41
- Artificial Intelligence 56
- Computer Vision and Pattern Recognition 22
- Computer Graphics and Computer-Aided Design 2
- Computer Science Applications 3
- Statistical and Nonlinear Physics 5
Countries citing papers authored by I. Babuschkin
This map shows the geographic impact of I. Babuschkin'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 I. Babuschkin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites I. Babuschkin more than expected).
Fields of papers citing papers by I. Babuschkin
This network shows the impact of papers produced by I. Babuschkin. 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 I. Babuschkin. The network helps show where I. Babuschkin may publish in the future.
Co-authors
The 25 scholars most cited alongside I. Babuschkin, 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 | Deep reinforcement learning with relational inductive biases | 2018 | 60 |
| 2 | Synthesizing Programs for Images using Reinforced Adversarial Learning. | 2018 | 13 |
| 3 | Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer | 2021 | 6 |
| 4 | 2017 | 1 | |
| 5 | 2015 | 1 | |
| 6 | 2017 | 0 |
About I. Babuschkin
I. Babuschkin is a scholar working on Artificial Intelligence, Biophysics, Information Systems, Computer Vision and Pattern Recognition and Information Systems and Management, having authored 6 papers that have together received 81 indexed citations. Recurring topics across this work include Cell Image Analysis Techniques (2 papers), Research Data Management Practices (1 paper), Scientific Computing and Data Management (1 paper), EEG and Brain-Computer Interfaces (1 paper), Adversarial Robustness in Machine Learning (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper), Neural Networks and Applications (1 paper) and Computational Physics and Python Applications (1 paper). The work is most often cited by research in Artificial Intelligence (56 citations), Computer Vision and Pattern Recognition (22 citations), Computer Graphics and Computer-Aided Design (2 citations), Computer Science Applications (3 citations) and Statistical and Nonlinear Physics (5 citations). I. Babuschkin has collaborated with scholars based in United States, Australia and Canada. Frequent co-authors include Oriol Vinyals, Karl Tuyls, David Raposo, Murray Shanahan, Edward Lockhart, Victoria Langston, Razvan Pascanu, Yujia Li, David Reichert and Vinícius Zambaldi. Their work appears in journals such as Journal of Physics Conference Series, International Conference on Machine Learning, International Conference on Learning Representations, Zenodo (CERN European Organization for Nuclear Research) and Neural Information Processing Systems.
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.