Tom Schaul
- Artificial Intelligence top 0.2%
- Evolutionary Algorithms and Applications 29
- Reinforcement Learning in Robotics 25
- Metaheuristic Optimization Algorithms Research 17
- Artificial Intelligence in Games 12
- Neural Networks and Applications 9
- Automotive Engineering top 2%
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- Advanced Multi-Objective Optimization Algorithms 5
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- Blind Source Separation Techniques 8
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- Digital Games and Media 6
- Co-authors
- Matteo HesselHado van HasseltMarc LanctotDavid SilverNando de FreitasZiyu WangGeorg OstrovskiBilal Piot
- Journals
- IEEE Transactions on Computational Intelligence and AI in Games (2 papers)Molecular Pharmaceutics (1 paper)Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment (1 paper)
- Partner nations
- United StatesUnited KingdomSwitzerland
In The Last Decade
Tom Schaul
51 papers receiving 4.7k citations
Hit Papers
Peers
Comparison fields: 5 of 132
- Artificial Intelligence 3.3k
- Computer Vision and Pattern Recognition 877
- Automotive Engineering 429
- Control and Systems Engineering 824
- Computational Theory and Mathematics 471
Countries citing papers authored by Tom Schaul
This map shows the geographic impact of Tom Schaul'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 Tom Schaul with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom Schaul more than expected).
Fields of papers citing papers by Tom Schaul
This network shows the impact of papers produced by Tom Schaul. 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 Tom Schaul. The network helps show where Tom Schaul may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Tom Schaul, 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 | 2023 | 10 | |
| 2 | 2023 | 12 | |
| 3 | Representation in Evolutionary Computation for Games | 2019 | 1 |
| 4 | Natural Value Approximators: Learning when to Trust Past Estimates | 2017 | 0 |
| 5 | The predictron: end-to-end learning and planning | 2017 | 25 |
| 6 | Learning from Demonstrations for Real World Reinforcement Learning | 2017 | 43 |
| 7 | 2017 | 110 | |
| 8 | Unifying count-based exploration and intrinsic motivationbreakdown → | 2016 | 280 |
| 9 | 2016 | 110 | |
| 10 | Adaptive learning rates and parallelization for stochastic, sparse, non-smooth gradients | 2013 | 8 |
| 11 | 2013 | 130 | |
| 12 | 2012 | 4 | |
| 13 | 2012 | 0 | |
| 14 | 2011 | 6 | |
| 15 | Studies in Continuous Black-box Optimization | 2011 | 6 |
| 16 | 2010 | 5 | |
| 17 | 2010 | 2 | |
| 18 | 2008 | 126 | |
| 19 | Natural Evolution Strategies | 2008 | 152 |
| 20 | 2006 | 3 |
About Tom Schaul
Tom Schaul is a scholar working on Artificial Intelligence, Signal Processing and Computational Theory and Mathematics, having authored 54 papers that have together received 4.9k indexed citations. Recurring topics across this work include Evolutionary Algorithms and Applications (29 papers), Reinforcement Learning in Robotics (25 papers), Metaheuristic Optimization Algorithms Research (17 papers), Artificial Intelligence in Games (12 papers), Neural Networks and Applications (9 papers), Blind Source Separation Techniques (8 papers), Digital Games and Media (6 papers) and Advanced Multi-Objective Optimization Algorithms (5 papers). The work is most often cited by research in Artificial Intelligence (3.3k citations), Computer Vision and Pattern Recognition (877 citations) and Automotive Engineering (429 citations). Tom Schaul has collaborated with scholars based in United States, United Kingdom and Switzerland. Frequent co-authors include Matteo Hessel, Hado van Hasselt, Marc Lanctot, David Silver, Nando de Freitas, Ziyu Wang, Georg Ostrovski, Bilal Piot, Dan Horgan and Will Dabney. Their work appears in journals such as IEEE Transactions on Computational Intelligence and AI in Games, Molecular Pharmaceutics, Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment, Journal of Machine Learning Research and Paladyn Journal of Behavioral Robotics.
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.