Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Auto-Encoding Variational Bayes
20138.9k citationsDiederik P. Kingma, Max WellingUvA-DARE (University of Amsterdam)profile →
An Introduction to Variational Autoencoders
20191.4k citationsDiederik P. Kingma, Max WellingarXiv (Cornell University)profile →
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).
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-authorship network of co-authors of Max Welling
This figure shows the co-authorship network connecting the top 25 collaborators of Max Welling.
A scholar is included among the top collaborators of Max Welling based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Max Welling. Max Welling is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kool, Wouter, Herke van Hoof, & Max Welling. (2019). Buy 4 REINFORCE Samples, Get a Baseline for Free!. International Conference on Learning Representations.7 indexed citations
Welling, Max, et al.. (2012). The Time-Marginalized Coalescent Prior for Hierarchical Clustering. UvA-DARE (University of Amsterdam). 25. 2969–2977.2 indexed citations
9.
Welling, Max, et al.. (2012). A cluster-cumulant expansion at the fixed points of belief propagation. arXiv (Cornell University). 883–892.3 indexed citations
10.
Newman, David, Arthur Asuncion, Padhraic Smyth, & Max Welling. (2009). Distributed Algorithms for Topic Models. Journal of Machine Learning Research. 10(62). 1801–1828.251 indexed citations
11.
Porteous, Ian R., Evgeniy Bart, & Max Welling. (2008). Multi-HDP: a non parametric Bayesian model for tensor factorization. National Conference on Artificial Intelligence. 1487–1490.56 indexed citations
12.
Smyth, Padhraic, Max Welling, & Arthur Asuncion. (2008). Asynchronous Distributed Learning of Topic Models. Neural Information Processing Systems. 21. 81–88.87 indexed citations
13.
Teh, Yee Whye, Kenichi Kurihara, & Max Welling. (2007). Collapsed Variational Inference for HDP. UCL Discovery (University College London). 20. 1481–1488.86 indexed citations
14.
Newman, David, Padhraic Smyth, Max Welling, & Arthur Asuncion. (2007). Distributed Inference for Latent Dirichlet Allocation. Neural Information Processing Systems. 20. 1081–1088.133 indexed citations
15.
Welling, Max. (2005). Robust Higher Order Statistics.. International Conference on Artificial Intelligence and Statistics.21 indexed citations
Teh, Yee Whye & Max Welling. (2003). On Improving the Efficiency of the Iterative Proportional Fitting Procedure. International Conference on Artificial Intelligence and Statistics. 262–269.15 indexed citations
19.
Welling, Max, Richard S. Zemel, & Geoffrey E. Hinton. (2002). Self Supervised Boosting. Neural Information Processing Systems. 15. 681–688.23 indexed citations
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.