Jack W. Rae
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Explainable Artificial Intelligence (XAI)
- Domain Adaptation and Few-Shot Learning
- Neural Networks and Applications
- Reinforcement Learning in Robotics
- Adversarial Robustness in Machine Learning
Papers in
-
- Topic Modeling 4
- Neural Networks and Applications 3
- Domain Adaptation and Few-Shot Learning 3
- Reinforcement Learning in Robotics 2
- Machine Learning and ELM 1
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- Advanced Neural Network Applications 4
- Multimodal Machine Learning Applications 3
- Co-authors
- Ali Razavi (1 shared paper)Po-Sen Huang (1 shared paper)Dani Yogatama (1 shared paper)Johannes Welbl (1 shared paper)Robert Stanforth (1 shared paper)Pushmeet Kohli (1 shared paper)Huan Zhang (1 shared paper)Scott Reed (1 shared paper)
- Journals
- International Conference on Learning Representations (2 papers)arXiv (Cornell University) (5 papers)International Conference on Machine Learning (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Jack W. Rae
10 papers receiving 176 citations
Peers
Comparison fields: 5 of 54
- Artificial Intelligence 138
- Health Informatics 5
- Computational Mathematics 2
- General Social Sciences 9
- Computer Vision and Pattern Recognition 48
Countries citing papers authored by Jack W. Rae
This map shows the geographic impact of Jack W. Rae'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 Jack W. Rae with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jack W. Rae more than expected).
Fields of papers citing papers by Jack W. Rae
This network shows the impact of papers produced by Jack W. Rae. 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 Jack W. Rae. The network helps show where Jack W. Rae may publish in the future.
Co-authors
The 25 scholars most cited alongside Jack W. Rae, 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 | 2020 | 74 | |
| 2 | 2018 | 27 | |
| 3 | 2016 | 25 | |
| 4 | Multiplicative Interactions and Where to Find Them | 2020 | 20 |
| 5 | 2020 | 20 | |
| 6 | Stabilizing Transformers for Reinforcement Learning | 2020 | 9 |
| 7 | V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control | 2020 | 3 |
| 8 | 2019 | 3 | |
| 9 | 2021 | 2 | |
| 10 | Meta-Learning Deep Energy-Based Memory Models | 2020 | 1 |
About Jack W. Rae
Jack W. Rae is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Statistical and Nonlinear Physics and Computational Theory and Mathematics, having authored 10 papers that have together received 184 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (4 papers), Topic Modeling (4 papers), Multimodal Machine Learning Applications (3 papers), Neural Networks and Applications (3 papers), Domain Adaptation and Few-Shot Learning (3 papers), Reinforcement Learning in Robotics (2 papers), Single-cell and spatial transcriptomics (1 paper) and Machine Learning and ELM (1 paper). The work is most often cited by research in Artificial Intelligence (138 citations), Health Informatics (5 citations), Computational Mathematics (2 citations), General Social Sciences (9 citations) and Computer Vision and Pattern Recognition (48 citations). Jack W. Rae has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Ali Razavi, Po-Sen Huang, Dani Yogatama, Johannes Welbl, Robert Stanforth, Pushmeet Kohli, Huan Zhang, Scott Reed, Felix Hill and Phil Blunsom. Their work appears in journals such as International Conference on Learning Representations, arXiv (Cornell University) and International Conference on Machine Learning.
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.