David Duvenaud
Impact in
- Computational Theory and Mathematics top 0.5%
- Computational Drug Discovery Methods
- Artificial Intelligence top 2%
- Gaussian Processes and Bayesian Inference
- Topic Modeling
Papers in
-
- Gaussian Processes and Bayesian Inference 10
- Explainable Artificial Intelligence (XAI) 6
- Neural Networks and Applications 5
- Adversarial Robustness in Machine Learning 4
- Machine Learning in Healthcare 3
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- Generative Adversarial Networks and Image Synthesis 12
- Co-authors
- Ryan P. Adams (6 shared papers)Timothy Hirzel (1 shared paper)Benjamín Sánchez-Lengeling (1 shared paper)Dennis Sheberla (1 shared paper)Rafael Gómez‐Bombarelli (1 shared paper)Jorge Aguilera‐Iparraguirre (1 shared paper)Alán Aspuru‐Guzik (1 shared paper)José Miguel Hernández-Lobato (1 shared paper)
- Journals
- Computer Graphics Forum (1 paper)Journal of Machine Learning Research (1 paper)Cognitive Psychology (1 paper)Digital Access to Scholarship at Harvard (DASH) (Harvard University) (1 paper)cIRcle (University of British Columbia) (1 paper)
- Partner nations
- CanadaUnited StatesUnited Kingdom
In The Last Decade
David Duvenaud
40 papers receiving 2.0k citations
David Duvenaud's Hit Papers
Peers
Comparison fields: 5 of 154
- Computational Theory and Mathematics 864
- Artificial Intelligence 657
- Materials Chemistry 749
- Computer Vision and Pattern Recognition 247
- Statistical and Nonlinear Physics 148
Countries citing papers authored by David Duvenaud
This map shows the geographic impact of David Duvenaud'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 David Duvenaud with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Duvenaud more than expected).
Fields of papers citing papers by David Duvenaud
This network shows the impact of papers produced by David Duvenaud. 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 David Duvenaud. The network helps show where David Duvenaud may publish in the future.
Co-authors
The 25 scholars most cited alongside David Duvenaud, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 41 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. Hit paper breakdown → | 2018 | 1145 |
| 2 | Latent Ordinary Differential Equations for Irregularly-Sampled Time Series | 2019 | 122 |
| 3 | 2011 | 115 | |
| 4 | 2019 | 73 | |
| 5 | 2014 | 70 | |
| 6 | 2019 | 60 | |
| 7 | 2019 | 59 | |
| 8 | Composing graphical models with neural networks for structured representations and fast inference | 2016 | 58 |
| 9 | 2017 | 43 | |
| 10 | Invertible Residual Networks | 2019 | 39 |
| 11 | 2016 | 32 | |
| 12 | 2014 | 32 | |
| 13 | 2018 | 29 | |
| 14 | Active Learning of Model Evidence Using Bayesian Quadrature | 2012 | 25 |
| 15 | 2017 | 23 | |
| 16 | Early Stopping as Nonparametric Variational Inference | 2016 | 22 |
| 17 | Residual Flows for Invertible Generative Modeling | 2019 | 18 |
| 18 | 2014 | 18 | |
| 19 | Isolating Sources of Disentanglement in Variational Autoencoders. | 2018 | 16 |
| 20 | What went wrong and when? Instance-wise feature importance for time-series black-box models | 2020 | 14 |
About David Duvenaud
David Duvenaud is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Signal Processing and Control and Systems Engineering, having authored 41 papers that have together received 2.1k indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (12 papers), Gaussian Processes and Bayesian Inference (10 papers), Model Reduction and Neural Networks (7 papers), Explainable Artificial Intelligence (XAI) (6 papers), Neural Networks and Applications (5 papers), Time Series Analysis and Forecasting (5 papers), Adversarial Robustness in Machine Learning (4 papers) and Machine Learning in Healthcare (3 papers). The work is most often cited by research in Computational Theory and Mathematics (864 citations), Artificial Intelligence (657 citations), Materials Chemistry (749 citations), Computer Vision and Pattern Recognition (247 citations) and Statistical and Nonlinear Physics (148 citations). David Duvenaud has collaborated with scholars based in Canada, United States and United Kingdom. Frequent co-authors include Ryan P. Adams, Timothy Hirzel, Benjamín Sánchez-Lengeling, Dennis Sheberla, Rafael Gómez‐Bombarelli, Jorge Aguilera‐Iparraguirre, Alán Aspuru‐Guzik, José Miguel Hernández-Lobato, Jennifer N. Wei and Ricky T. Q. Chen. Their work appears in journals such as Computer Graphics Forum, Journal of Machine Learning Research, Cognitive Psychology, Digital Access to Scholarship at Harvard (DASH) (Harvard University) and cIRcle (University of British Columbia).
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.