Max Welling
Impact in
- Computer Vision and Pattern Recognition top 0.05%
- Generative Adversarial Networks and Image Synthesis
- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Artificial Intelligence top 0.02%
- Topic Modeling
- Anomaly Detection Techniques and Applications
- Domain Adaptation and Few-Shot Learning
- Gaussian Processes and Bayesian Inference
- Natural Language Processing Techniques
Papers in
-
- Gaussian Processes and Bayesian Inference 37
- Bayesian Methods and Mixture Models 36
- Neural Networks and Applications 34
- Domain Adaptation and Few-Shot Learning 17
- Machine Learning and Algorithms 16
- Bayesian Modeling and Causal Inference 13
- Topic Modeling 11
-
- Generative Adversarial Networks and Image Synthesis 24
- Co-authors
- Diederik P. Kingma (8 shared papers)Yee Whye Teh (9 shared papers)Zoubin Ghahramani (5 shared papers)Kilian Q. Weinberger (5 shared papers)Nicu Sebe (8 shared papers)Arthur Asuncion (7 shared papers)Padhraic Smyth (9 shared papers)Danilo Jimenez Rezende (1 shared paper)
- Journals
- Neural Computation (5 papers)Journal of Machine Learning Research (3 papers)Classical and Quantum Gravity (3 papers)The Astrophysical Journal (2 papers)Nuclear Physics B (1 paper)
- Partner nations
- United StatesNetherlandsUnited Kingdom
In The Last Decade
Max Welling
197 papers receiving 19.7k citations
Max Welling's Hit Papers
Peers
Comparison fields: 5 of 208
- Computer Vision and Pattern Recognition 8.1k
- Artificial Intelligence 10.4k
- Computational Mathematics 139
- Signal Processing 2.2k
- Computer Graphics and Computer-Aided Design 412
Countries citing papers authored by Max Welling
This map shows the geographic impact of Max Welling'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 Max Welling with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Max Welling more than expected).
Fields of papers citing papers by Max Welling
This network shows the impact of papers produced by Max Welling. 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 Max Welling. The network helps show where Max Welling may publish in the future.
Co-authors
The 25 scholars most cited alongside Max Welling, 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 204 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Auto-Encoding Variational Bayes Hit paper breakdown → | 2013 | 8865 |
| 2 | An Introduction to Variational Autoencoders Hit paper breakdown → | 2019 | 1357 |
| 3 | Proceedings of the 26th International Conference on Neural Information Processing Systems Hit paper breakdown → | 2013 | 1103 |
| 4 | Semi-Supervised Learning with Deep Generative Models Hit paper breakdown → | 2014 | 897 |
| 5 | Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 1 Hit paper breakdown → | 2014 | 574 |
| 6 | Bayesian Learning via Stochastic Gradient Langevin Dynamics Hit paper breakdown → | 2011 | 571 |
| 7 | Proceedings of the 27th International Conference on Neural Information Processing Systems Hit paper breakdown → | 2014 | 400 |
| 8 | Fast collapsed gibbs sampling for latent dirichlet allocation Hit paper breakdown → | 2008 | 370 |
| 9 | Improved Variational Inference with Inverse Autoregressive Flow Hit paper breakdown → | 2016 | 293 |
| 10 | Exponential Family Harmoniums with an Application to Information Retrieval Hit paper breakdown → | 2004 | 264 |
| 11 | 2012 | 263 | |
| 12 | Distributed Algorithms for Topic Models | 2009 | 251 |
| 13 | 2016 | 200 | |
| 14 | 2016 | 196 | |
| 15 | 2009 | 192 | |
| 16 | 2016 | 175 | |
| 17 | 2015 | 160 | |
| 18 | 2016 | 151 | |
| 19 | 2019 | 141 | |
| 20 | Distributed Inference for Latent Dirichlet Allocation | 2007 | 133 |
About Max Welling
Max Welling is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Statistics and Probability and Molecular Biology, having authored 204 papers that have together received 20.6k indexed citations. Recurring topics across this work include Gaussian Processes and Bayesian Inference (37 papers), Bayesian Methods and Mixture Models (36 papers), Neural Networks and Applications (34 papers), Generative Adversarial Networks and Image Synthesis (24 papers), Domain Adaptation and Few-Shot Learning (17 papers), Machine Learning and Algorithms (16 papers), Bayesian Modeling and Causal Inference (13 papers) and Topic Modeling (11 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (8.1k citations), Artificial Intelligence (10.4k citations), Computational Mathematics (139 citations), Signal Processing (2.2k citations) and Computer Graphics and Computer-Aided Design (412 citations). Max Welling has collaborated with scholars based in United States, Netherlands and United Kingdom. Frequent co-authors include Diederik P. Kingma, Yee Whye Teh, Zoubin Ghahramani, Kilian Q. Weinberger, Nicu Sebe, Arthur Asuncion, Padhraic Smyth, Danilo Jimenez Rezende, Shakir Mohamed and Léon Bottou. Their work appears in journals such as Neural Computation, Journal of Machine Learning Research, Classical and Quantum Gravity, The Astrophysical Journal and Nuclear Physics B.
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.