Tim Harley
Impact in
- Artificial Intelligence top 2%
- Topic Modeling
- Neural Networks and Applications
- Domain Adaptation and Few-Shot Learning
- Reinforcement Learning in Robotics
- Neural Networks and Reservoir Computing
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- Multimodal Machine Learning Applications
Papers in
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- Domain Adaptation and Few-Shot Learning 2
- Topic Modeling 2
- Neural Networks and Applications 2
- Explainable Artificial Intelligence (XAI) 2
- Artificial Intelligence in Games 1
- Reinforcement Learning in Robotics 1
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- Advanced Neural Network Applications 1
- Co-authors
- Alex Graves (2 shared papers)Ivo Danihelka (2 shared papers)Greg Wayne (2 shared papers)Phil Blunsom (1 shared paper)John Agapiou (1 shared paper)Tiago Ramalho (1 shared paper)Agnieszka Grabska‐Barwińska (1 shared paper)Malcolm Reynolds (1 shared paper)
- Journals
- Ergonomics (1 paper)Nature (1 paper)European Conference on Artificial Intelligence (1 paper)arXiv (Cornell University) (1 paper)International Conference on Learning Representations (1 paper)
- Partner nations
- United KingdomUnited StatesAustralia
In The Last Decade
Tim Harley
7 papers receiving 769 citations
Tim Harley's Hit Papers
Peers
Comparison fields: 5 of 103
- Artificial Intelligence 515
- Computer Vision and Pattern Recognition 193
- Cognitive Neuroscience 92
- Computer Science Applications 22
- Computational Theory and Mathematics 62
Countries citing papers authored by Tim Harley
This map shows the geographic impact of Tim Harley'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 Tim Harley with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tim Harley more than expected).
Fields of papers citing papers by Tim Harley
This network shows the impact of papers produced by Tim Harley. 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 Tim Harley. The network helps show where Tim Harley may publish in the future.
Co-authors
The 25 scholars most cited alongside Tim Harley, 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 | Hybrid computing using a neural network with dynamic external memory Hit paper breakdown → | 2016 | 699 |
| 2 | 2019 | 27 | |
| 3 | The predictron: end-to-end learning and planning | 2017 | 25 |
| 4 | 2016 | 24 | |
| 5 | Multiplicative Interactions and Where to Find Them | 2020 | 21 |
| 6 | 1972 | 17 | |
| 7 | There's more than one way. | 1982 | 1 |
About Tim Harley
Tim Harley is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Electrical and Electronic Engineering and Infectious Diseases, having authored 7 papers that have together received 814 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (2 papers), Topic Modeling (2 papers), Neural Networks and Applications (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Artificial Intelligence in Games (1 paper), Advanced Memory and Neural Computing (1 paper), Advanced Neural Network Applications (1 paper) and Reinforcement Learning in Robotics (1 paper). The work is most often cited by research in Artificial Intelligence (515 citations), Computer Vision and Pattern Recognition (193 citations), Cognitive Neuroscience (92 citations), Computer Science Applications (22 citations) and Computational Theory and Mathematics (62 citations). Tim Harley has collaborated with scholars based in United Kingdom, United States and Australia. Frequent co-authors include Alex Graves, Ivo Danihelka, Greg Wayne, Phil Blunsom, John Agapiou, Tiago Ramalho, Agnieszka Grabska‐Barwińska, Malcolm Reynolds, Edward Grefenstette and Sergio Gómez Colmenarejo. Their work appears in journals such as Ergonomics, Nature, European Conference on Artificial Intelligence, arXiv (Cornell University) 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.