Ricky T. Q. Chen
- Artificial Intelligence top 10%
- Statistical and Nonlinear Physics top 10%
- Computer Vision and Pattern Recognition
- Signal Processing
- Computational Mechanics
- Co-authors
- David DuvenaudYulia RubanovaJoern-Henrik JacobsenJens BehrmannWill GrathwohlRodrigo A. Vargas–HernándezPaul BrumerXuechen Li
- Topics
- Generative Adversarial Networks and Image Synthesis (3 papers)Model Reduction and Neural Networks (3 papers)Music and Audio Processing (2 papers)
- Journals
- arXiv (Cornell University)Neural Information Processing SystemsInternational Conference on Machine Learning
- Partner nations
- CanadaUnited StatesGermany
In The Last Decade
Ricky T. Q. Chen
8 papers receiving 198 citations
Peers
Comparison fields: 5 of 70
- Artificial Intelligence 120
- Statistical and Nonlinear Physics 58
- Computer Vision and Pattern Recognition 51
- Signal Processing 31
- Computational Mechanics 15
Countries citing papers authored by Ricky T. Q. Chen
This map shows the geographic impact of Ricky T. Q. Chen'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 Ricky T. Q. Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ricky T. Q. Chen more than expected).
Fields of papers citing papers by Ricky T. Q. Chen
This network shows the impact of papers produced by Ricky T. Q. Chen. 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 Ricky T. Q. Chen. The network helps show where Ricky T. Q. Chen may publish in the future.
Co-authorship network of co-authors of Ricky T. Q. Chen
This figure shows the co-authorship network connecting the top 25 collaborators of Ricky T. Q. Chen. A scholar is included among the top collaborators of Ricky T. Q. Chen based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ricky T. Q. Chen. Ricky T. Q. Chen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 15 | |
| 3 | Scalable Gradients for Stochastic Differential Equations | 5 |
| 4 | 2 | |
| 5 | Residual Flows for Invertible Generative Modeling | 18 |
| 6 | Latent Ordinary Differential Equations for Irregularly-Sampled Time Series | 120 |
| 7 | Invertible Residual Networks | 40 |
| 8 | Scalable Gradients and Variational Inference for Stochastic Differential Equations | 5 |
About Ricky T. Q. Chen
Ricky T. Q. Chen is a scholar working on Statistical and Nonlinear Physics, Signal Processing and Artificial Intelligence, having authored 8 papers that have together received 206 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (3 papers), Model Reduction and Neural Networks (3 papers) and Music and Audio Processing (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (58 citations), Artificial Intelligence (120 citations) and Signal Processing (31 citations). Ricky T. Q. Chen has collaborated with scholars based in Canada, United States and Germany. Frequent co-authors include David Duvenaud, Yulia Rubanova, Joern-Henrik Jacobsen, Jens Behrmann, Will Grathwohl, Rodrigo A. Vargas–Hernández, Paul Brumer, Xuechen Li, Ting‐Kam Leonard Wong and Gabriel Synnaeve. Their work appears in journals such as arXiv (Cornell University), Neural Information Processing Systems and International Conference on Machine Learning.
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.