Sam Toyer
Impact in
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- Human Pose and Action Recognition
- Multimodal Machine Learning Applications
- Generative Adversarial Networks and Image Synthesis
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- AI-based Problem Solving and Planning
- Reinforcement Learning in Robotics
- Anomaly Detection Techniques and Applications
- Domain Adaptation and Few-Shot Learning
Papers in
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- Machine Learning and Algorithms 2
- AI-based Problem Solving and Planning 2
- Artificial Intelligence in Games 1
- Natural Language Processing Techniques 1
- Bayesian Modeling and Causal Inference 1
- Adversarial Robustness in Machine Learning 1
- Reinforcement Learning in Robotics 1
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- Video Surveillance and Tracking Methods 1
- Co-authors
- Felipe Trevizan (2 shared papers)Lexing Xie (2 shared papers)Sylvie Thiébaux (2 shared papers)Anoop Cherian (1 shared paper)Tengda Han (1 shared paper)Stephen Jay Gould (1 shared paper)Sergey Levine (1 shared paper)Pieter Abbeel (2 shared papers)
- Journals
- Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)Proceedings of the International Symposium on Combinatorial Search (1 paper)arXiv (Cornell University) (2 papers)
- Partner nations
- United StatesAustralia
In The Last Decade
Sam Toyer
6 papers receiving 94 citations
Peers
Comparison fields: 5 of 33
- Computer Vision and Pattern Recognition 42
- Artificial Intelligence 65
- Software 4
- Health Informatics 1
- Control and Systems Engineering 16
Countries citing papers authored by Sam Toyer
This map shows the geographic impact of Sam Toyer'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 Sam Toyer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sam Toyer more than expected).
Fields of papers citing papers by Sam Toyer
This network shows the impact of papers produced by Sam Toyer. 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 Sam Toyer. The network helps show where Sam Toyer may publish in the future.
Co-authors
The 24 scholars most cited alongside Sam Toyer, 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 | 2018 | 36 | |
| 2 | Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow | 2018 | 25 |
| 3 | 2017 | 23 | |
| 4 | 2025 | 7 | |
| 5 | 2021 | 5 | |
| 6 | The MAGICAL Benchmark for Robust Imitation | 2020 | 1 |
| 7 | 2024 | 0 |
About Sam Toyer
Sam Toyer is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering, Media Technology and Infectious Diseases, having authored 7 papers that have together received 97 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (2 papers), AI-based Problem Solving and Planning (2 papers), Artificial Intelligence in Games (1 paper), Video Surveillance and Tracking Methods (1 paper), Natural Language Processing Techniques (1 paper), Bayesian Modeling and Causal Inference (1 paper), Adversarial Robustness in Machine Learning (1 paper) and Reinforcement Learning in Robotics (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (42 citations), Artificial Intelligence (65 citations), Software (4 citations), Health Informatics (1 citation) and Control and Systems Engineering (16 citations). Sam Toyer has collaborated with scholars based in United States and Australia. Frequent co-authors include Felipe Trevizan, Lexing Xie, Sylvie Thiébaux, Anoop Cherian, Tengda Han, Stephen Jay Gould, Sergey Levine, Pieter Abbeel, Xue Bin Peng and Angjoo Kanazawa. Their work appears in journals such as Proceedings of the AAAI Conference on Artificial Intelligence, Proceedings of the International Symposium on Combinatorial Search 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.