Michael U. Gutmann
Impact in
- Artificial Intelligence top 1%
- Topic Modeling
- Natural Language Processing Techniques
- Domain Adaptation and Few-Shot Learning
- Gaussian Processes and Bayesian Inference
- Advanced Graph Neural Networks
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- Multimodal Machine Learning Applications
- Generative Adversarial Networks and Image Synthesis
Papers in
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- Gaussian Processes and Bayesian Inference 9
- Neural Networks and Applications 6
- Bayesian Methods and Mixture Models 5
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- Neural dynamics and brain function 7
- Visual perception and processing mechanisms 6
- Co-authors
- Aapo Hyvärinen (22 shared papers)Jukka Corander (11 shared papers)Jun-ichiro Hirayama (2 shared papers)Samuel Kaski (5 shared papers)Ritabrata Dutta (3 shared papers)William P. Hanage (4 shared papers)Charles Sutton (2 shared papers)Akash Srivastava (2 shared papers)
- Journals
- Journal of Machine Learning Research (4 papers)Genetics (2 papers)Journal of The Royal Society Interface (1 paper)Scientific Reports (1 paper)Microbial Genomics (1 paper)
- Partner nations
- FinlandUnited KingdomJapan
In The Last Decade
Michael U. Gutmann
50 papers receiving 1.8k citations
Michael U. Gutmann's Hit Papers
Peers
Comparison fields: 5 of 153
- Artificial Intelligence 1.1k
- Computer Vision and Pattern Recognition 507
- Statistics and Probability 165
- Signal Processing 130
- Statistical and Nonlinear Physics 107
Countries citing papers authored by Michael U. Gutmann
This map shows the geographic impact of Michael U. Gutmann'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 Michael U. Gutmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael U. Gutmann more than expected).
Fields of papers citing papers by Michael U. Gutmann
This network shows the impact of papers produced by Michael U. Gutmann. 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 Michael U. Gutmann. The network helps show where Michael U. Gutmann may publish in the future.
Co-authors
The 25 scholars most cited alongside Michael U. Gutmann, 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 51 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Noise-contrastive estimation: A new estimation principle for unnormalized statistical models Hit paper breakdown → | 2010 | 626 |
| 2 | 2012 | 313 | |
| 3 | 2016 | 105 | |
| 4 | Proceedings of Conference on Uncertainty in Artificial Intelligence (UAI 2011) | 2011 | 103 |
| 5 | 2016 | 97 | |
| 6 | VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning | 2017 | 96 |
| 7 | 2017 | 74 | |
| 8 | 1991 | 71 | |
| 9 | 2018 | 37 | |
| 10 | 2017 | 37 | |
| 11 | 2005 | 28 | |
| 12 | 2015 | 28 | |
| 13 | 2018 | 17 | |
| 14 | Adaptable Pouring: Teaching Robots Not to Spill using Fast but Approximate Fluid Simulation | 2017 | 16 |
| 15 | 2014 | 15 | |
| 16 | Proc. Conf. on Uncertainty in Artificial Intelligence (UAI) | 2010 | 15 |
| 17 | 2016 | 14 | |
| 18 | 2013 | 13 | |
| 19 | 2022 | 12 | |
| 20 | Bregman divergence as general framework to estimate unnormalized statistical models | 2011 | 12 |
About Michael U. Gutmann
Michael U. Gutmann is a scholar working on Artificial Intelligence, Cognitive Neuroscience, Signal Processing, Statistics and Probability and Computer Vision and Pattern Recognition, having authored 51 papers that have together received 1.9k indexed citations. Recurring topics across this work include Gaussian Processes and Bayesian Inference (9 papers), Blind Source Separation Techniques (9 papers), Neural dynamics and brain function (7 papers), Visual perception and processing mechanisms (6 papers), Neural Networks and Applications (6 papers), Bayesian Methods and Mixture Models (5 papers), Markov Chains and Monte Carlo Methods (5 papers) and Statistical Methods and Inference (4 papers). The work is most often cited by research in Artificial Intelligence (1.1k citations), Computer Vision and Pattern Recognition (507 citations), Statistics and Probability (165 citations), Signal Processing (130 citations) and Statistical and Nonlinear Physics (107 citations). Michael U. Gutmann has collaborated with scholars based in Finland, United Kingdom and Japan. Frequent co-authors include Aapo Hyvärinen, Jukka Corander, Jun-ichiro Hirayama, Samuel Kaski, Ritabrata Dutta, William P. Hanage, Charles Sutton, Akash Srivastava, Chris Russell and Lazar Valkov. Their work appears in journals such as Journal of Machine Learning Research, Genetics, Journal of The Royal Society Interface, Scientific Reports and Microbial Genomics.
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.