Aidan Clark
Impact in
- Atmospheric Science top 5%
- Meteorological Phenomena and Simulations
- Precipitation Measurement and Analysis
- Tropical and Extratropical Cyclones Research
- Cryospheric studies and observations
- Global and Planetary Change top 5%
- Flood Risk Assessment and Management
- Climate variability and models
Papers in
-
- Multimodal Machine Learning Applications 1
- Generative Adversarial Networks and Image Synthesis 1
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- Reinforcement Learning in Robotics 2
- Authorship Attribution and Profiling 1
- Co-authors
- Karen SimonyanKarel LencAmol MandhaneM. A. FitzsimonsSuman RavuriMaria AthanassiadouRaia HadsellAlberto Arribas
- Journals
- Nature (1 paper)International Conference on Machine Learning (1 paper)arXiv (Cornell University) (2 papers)
- Partner nations
- United KingdomUnited States
In The Last Decade
Aidan Clark
5 papers receiving 573 citations
Hit Papers
Peers
Comparison fields: 5 of 83
- Atmospheric Science 359
- Global and Planetary Change 275
- Environmental Engineering 125
- Computer Vision and Pattern Recognition 62
- Artificial Intelligence 90
Countries citing papers authored by Aidan Clark
This map shows the geographic impact of Aidan Clark'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 Aidan Clark with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aidan Clark more than expected).
Fields of papers citing papers by Aidan Clark
This network shows the impact of papers produced by Aidan Clark. 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 Aidan Clark. The network helps show where Aidan Clark may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Aidan Clark, 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 | Skilful precipitation nowcasting using deep generative models of radar Hit paper breakdown → | 2021 | 554 |
| 2 | V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control | 2020 | 3 |
| 3 | Stabilizing Transformers for Reinforcement Learning | 2020 | 9 |
| 4 | Efficient Video Generation on Complex Datasets. | 2019 | 24 |
| 5 | 2016 | 3 |
About Aidan Clark
Aidan Clark is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Cell Biology, Atmospheric Science and Global and Planetary Change, having authored 5 papers that have together received 593 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (2 papers), Zebrafish Biomedical Research Applications (1 paper), Authorship Attribution and Profiling (1 paper), Spam and Phishing Detection (1 paper), Single-cell and spatial transcriptomics (1 paper), Multimodal Machine Learning Applications (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper) and Precipitation Measurement and Analysis (1 paper). The work is most often cited by research in Atmospheric Science (359 citations), Global and Planetary Change (275 citations), Environmental Engineering (125 citations), Computer Vision and Pattern Recognition (62 citations) and Artificial Intelligence (90 citations). Aidan Clark has collaborated with scholars based in United Kingdom and United States. Frequent co-authors include Karen Simonyan, Karel Lenc, Amol Mandhane, M. A. Fitzsimons, Suman Ravuri, Maria Athanassiadou, Raia Hadsell, Alberto Arribas, Andrew Brock and Piotr Mirowski. Their work appears in journals such as Nature, International Conference on Machine Learning and arXiv (Cornell University).
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.