Max Welling
Impact in
- Artificial Intelligence top 0.05%
- Topic Modeling
- Advanced Graph Neural Networks
- Domain Adaptation and Few-Shot Learning
- Anomaly Detection Techniques and Applications
- Natural Language Processing Techniques
- Gaussian Processes and Bayesian Inference
- Computational Mathematics top 0.5%
Papers in
-
- Bayesian Methods and Mixture Models 37
- Gaussian Processes and Bayesian Inference 35
- Neural Networks and Applications 26
- Bayesian Modeling and Causal Inference 14
- Machine Learning and Algorithms 14
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- Generative Adversarial Networks and Image Synthesis 23
- Image Retrieval and Classification Techniques 15
- Co-authors
- Diederik P. Kingma (6 shared papers)Thomas Kipf (4 shared papers)Rianne van den Berg (2 shared papers)Michael Schlichtkrull (1 shared paper)Peter Bloem (1 shared paper)Ivan S. Titov (1 shared paper)Yee Whye Teh (10 shared papers)Zoubin Ghahramani (5 shared papers)
- Journals
- Neural Computation (5 papers)Journal of Machine Learning Research (3 papers)Classical and Quantum Gravity (3 papers)Medical Image Analysis (2 papers)The Astrophysical Journal (2 papers)
- Partner nations
- NetherlandsUnited StatesUnited Kingdom
In The Last Decade
Max Welling
195 papers receiving 17.2k citations
Max Welling's Hit Papers
Peers
Comparison fields: 5 of 213
- Artificial Intelligence 9.5k
- Computational Mathematics 161
- Computer Vision and Pattern Recognition 5.1k
- Health Informatics 178
- Signal Processing 1.3k
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 201 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Modeling Relational Data with Graph Convolutional Networks Hit paper breakdown → | 2018 | 3325 |
| 2 | An Introduction to Variational Autoencoders Hit paper breakdown → | 2019 | 1685 |
| 3 | Proceedings of the 26th International Conference on Neural Information Processing Systems Hit paper breakdown → | 2013 | 1186 |
| 4 | Scientific discovery in the age of artificial intelligence Hit paper breakdown → | 2023 | 1070 |
| 5 | Semi-Supervised Learning with Deep Generative Models Hit paper breakdown → | 2014 | 988 |
| 6 | Bayesian Learning via Stochastic Gradient Langevin Dynamics Hit paper breakdown → | 2012 | 657 |
| 7 | Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 1 Hit paper breakdown → | 2014 | 607 |
| 8 | 2000 | 464 | |
| 9 | Proceedings of the 27th International Conference on Neural Information Processing Systems Hit paper breakdown → | 2014 | 440 |
| 10 | Fast collapsed gibbs sampling for latent dirichlet allocation Hit paper breakdown → | 2008 | 414 |
| 11 | Improved Variational Inference with Inverse Autoregressive Flow Hit paper breakdown → | 2016 | 373 |
| 12 | 2018 | 301 | |
| 13 | Exponential Family Harmoniums with an Application to Information Retrieval | 2004 | 290 |
| 14 | Distributed Algorithms for Topic Models | 2009 | 278 |
| 15 | 2020 | 269 | |
| 16 | 2009 | 241 | |
| 17 | 2019 | 176 | |
| 18 | 2016 | 171 | |
| 19 | 2002 | 155 | |
| 20 | Distributed Inference for Latent Dirichlet Allocation | 2007 | 151 |
About Max Welling
Max Welling is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Statistics and Probability, Signal Processing and Statistical and Nonlinear Physics, having authored 201 papers that have together received 17.8k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (37 papers), Gaussian Processes and Bayesian Inference (35 papers), Neural Networks and Applications (26 papers), Generative Adversarial Networks and Image Synthesis (23 papers), Image Retrieval and Classification Techniques (15 papers), Bayesian Modeling and Causal Inference (14 papers), Markov Chains and Monte Carlo Methods (14 papers) and Machine Learning and Algorithms (14 papers). The work is most often cited by research in Artificial Intelligence (9.5k citations), Computational Mathematics (161 citations), Computer Vision and Pattern Recognition (5.1k citations), Health Informatics (178 citations) and Signal Processing (1.3k citations). Max Welling has collaborated with scholars based in Netherlands, United States and United Kingdom. Frequent co-authors include Diederik P. Kingma, Thomas Kipf, Rianne van den Berg, Michael Schlichtkrull, Peter Bloem, Ivan S. Titov, Yee Whye Teh, Zoubin Ghahramani, Kilian Q. Weinberger and Danilo Jimenez Rezende. Their work appears in journals such as Neural Computation, Journal of Machine Learning Research, Classical and Quantum Gravity, Medical Image Analysis and The Astrophysical Journal.
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.