Jonathan Schwarz
Impact in
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- Human Pose and Action Recognition
- Multimodal Machine Learning Applications
- Video Analysis and Summarization
- Generative Adversarial Networks and Image Synthesis
Papers in
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- Neural Networks and Applications 3
- Machine Learning and Algorithms 2
- Domain Adaptation and Few-Shot Learning 2
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- Gene expression and cancer classification 1
- Co-authors
- Taku Komura (1 shared paper)Ikhsanul Habibie (1 shared paper)Daniel Holden (1 shared paper)Timothy Lillicrap (1 shared paper)Arun Ahuja (1 shared paper)David Rolnick (1 shared paper)Razvan Pascanu (3 shared papers)Yee Whye Teh (3 shared papers)
- Journals
- Canadian Pharmacists Journal / Revue des Pharmaciens du Canada (1 paper)Entropy (1 paper)Edinburgh Research Explorer (1 paper)PubMed (1 paper)International Conference on Learning Representations (1 paper)
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
Jonathan Schwarz
7 papers receiving 179 citations
Peers
Comparison fields: 5 of 51
- Computer Vision and Pattern Recognition 129
- Computational Mathematics 2
- Control and Systems Engineering 75
- Artificial Intelligence 94
- Human-Computer Interaction 7
Countries citing papers authored by Jonathan Schwarz
This map shows the geographic impact of Jonathan Schwarz'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 Jonathan Schwarz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan Schwarz more than expected).
Fields of papers citing papers by Jonathan Schwarz
This network shows the impact of papers produced by Jonathan Schwarz. 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 Jonathan Schwarz. The network helps show where Jonathan Schwarz may publish in the future.
Co-authors
The 24 scholars most cited alongside Jonathan Schwarz, 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 | 2017 | 97 | |
| 2 | Experience Replay for Continual Learning | 2019 | 54 |
| 3 | Multiplicative Interactions and Where to Find Them | 2020 | 21 |
| 4 | Functional Regularisation for Continual Learning with Gaussian Processes | 2020 | 9 |
| 5 | 2019 | 4 | |
| 6 | 2019 | 3 | |
| 7 | Discrimination of normal and dyskaryotic cells with a new high-resolution system (CIALIS). | 1983 | 1 |
| 8 | 2023 | 0 |
About Jonathan Schwarz
Jonathan Schwarz is a scholar working on Artificial Intelligence, Molecular Biology, Pharmacology, Pediatrics, Perinatology and Child Health and Control and Systems Engineering, having authored 8 papers that have together received 189 indexed citations. Recurring topics across this work include Neural Networks and Applications (3 papers), Machine Learning and Algorithms (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Hand Gesture Recognition Systems (1 paper), Gene expression and cancer classification (1 paper), Markov Chains and Monte Carlo Methods (1 paper), Human Motion and Animation (1 paper) and Prenatal Substance Exposure Effects (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (129 citations), Computational Mathematics (2 citations), Control and Systems Engineering (75 citations), Artificial Intelligence (94 citations) and Human-Computer Interaction (7 citations). Jonathan Schwarz has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Taku Komura, Ikhsanul Habibie, Daniel Holden, Timothy Lillicrap, Arun Ahuja, David Rolnick, Razvan Pascanu, Yee Whye Teh, Wojciech Marian Czarnecki and Siddhant M. Jayakumar. Their work appears in journals such as Canadian Pharmacists Journal / Revue des Pharmaciens du Canada, Entropy, Edinburgh Research Explorer, PubMed 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.