Max Welling
Impact in
- Computer Vision and Pattern Recognition top 0.05%
- Generative Adversarial Networks and Image Synthesis
- Advanced Image and Video Retrieval Techniques
- Advanced Neural Network Applications
- Artificial Intelligence top 0.02%
- Topic Modeling
- Advanced Graph Neural Networks
- Domain Adaptation and Few-Shot Learning
- Anomaly Detection Techniques and Applications
- Natural Language Processing Techniques
Papers in
-
- Bayesian Methods and Mixture Models 39
- Gaussian Processes and Bayesian Inference 38
- Neural Networks and Applications 35
- Machine Learning and Algorithms 17
- Domain Adaptation and Few-Shot Learning 16
- Bayesian Modeling and Causal Inference 15
-
- Generative Adversarial Networks and Image Synthesis 26
- Image Retrieval and Classification Techniques 15
- Co-authors
- Diederik P. Kingma (8 shared papers)Thomas Kipf (3 shared papers)Yee Whye Teh (10 shared papers)Rianne van den Berg (2 shared papers)Peter Bloem (1 shared paper)Michael Schlichtkrull (1 shared paper)Ivan Titov (1 shared paper)Kilian Q. Weinberger (5 shared papers)
- Journals
- Neural Computation (5 papers)Classical and Quantum Gravity (3 papers)Journal of Machine Learning Research (3 papers)The Astrophysical Journal (2 papers)Lecture notes in computer science (17 papers)
- Partner nations
- United StatesNetherlandsUnited Kingdom
In The Last Decade
Max Welling
212 papers receiving 25.1k citations
Max Welling's Hit Papers
Peers
Comparison fields: 5 of 209
- Computer Vision and Pattern Recognition 9.8k
- Artificial Intelligence 14.0k
- Computational Mathematics 170
- Signal Processing 2.5k
- Computer Graphics and Computer-Aided Design 446
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 221 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Auto-Encoding Variational Bayes Hit paper breakdown → | 2013 | 9416 |
| 2 | Modeling Relational Data with Graph Convolutional Networks Hit paper breakdown → | 2018 | 3094 |
| 3 | An Introduction to Variational Autoencoders Hit paper breakdown → | 2019 | 1555 |
| 4 | Proceedings of the 26th International Conference on Neural Information Processing Systems Hit paper breakdown → | 2013 | 1146 |
| 5 | Semi-Supervised Learning with Deep Generative Models Hit paper breakdown → | 2014 | 945 |
| 6 | Bayesian Learning via Stochastic Gradient Langevin Dynamics Hit paper breakdown → | 2011 | 625 |
| 7 | Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 1 Hit paper breakdown → | 2014 | 597 |
| 8 | 2000 | 475 | |
| 9 | Fast collapsed gibbs sampling for latent dirichlet allocation Hit paper breakdown → | 2008 | 420 |
| 10 | Proceedings of the 27th International Conference on Neural Information Processing Systems Hit paper breakdown → | 2014 | 414 |
| 11 | Improved Variational Inference with Inverse Autoregressive Flow Hit paper breakdown → | 2016 | 321 |
| 12 | Exponential Family Harmoniums with an Application to Information Retrieval Hit paper breakdown → | 2004 | 301 |
| 13 | On Smoothing and Inference for Topic Models Hit paper breakdown → | 2012 | 298 |
| 14 | Distributed Algorithms for Topic Models | 2009 | 275 |
| 15 | 2018 | 275 | |
| 16 | 2009 | 219 | |
| 17 | 2016 | 202 | |
| 18 | 2016 | 185 | |
| 19 | 2015 | 168 | |
| 20 | 2016 | 161 |
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 221 papers that have together received 26.2k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (39 papers), Gaussian Processes and Bayesian Inference (38 papers), Neural Networks and Applications (35 papers), Generative Adversarial Networks and Image Synthesis (26 papers), Machine Learning and Algorithms (17 papers), Domain Adaptation and Few-Shot Learning (16 papers), Bayesian Modeling and Causal Inference (15 papers) and Image Retrieval and Classification Techniques (15 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (9.8k citations), Artificial Intelligence (14.0k citations), Computational Mathematics (170 citations), Signal Processing (2.5k citations) and Computer Graphics and Computer-Aided Design (446 citations). Max Welling has collaborated with scholars based in United States, Netherlands and United Kingdom. Frequent co-authors include Diederik P. Kingma, Thomas Kipf, Yee Whye Teh, Rianne van den Berg, Peter Bloem, Michael Schlichtkrull, Ivan Titov, Kilian Q. Weinberger, Zoubin Ghahramani and Arthur Asuncion. Their work appears in journals such as Neural Computation, Classical and Quantum Gravity, Journal of Machine Learning Research, The Astrophysical Journal and Lecture notes in computer science.
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.