M
Max Welling
Researcher at University of Amsterdam
Publications - 464
Citations - 88808
Max Welling is an academic researcher from University of Amsterdam. The author has contributed to research in topics: Artificial neural network & Inference. The author has an hindex of 89, co-authored 441 publications receiving 64602 citations. Previous affiliations of Max Welling include University of California, Irvine & Bosch.
Papers
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Proceedings Article
Auto-Encoding Variational Bayes
Diederik P. Kingma,Max Welling +1 more
TL;DR: A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced.
Posted Content
Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf,Max Welling +1 more
TL;DR: A scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs which outperforms related methods by a significant margin.
Posted Content
Auto-Encoding Variational Bayes
Diederik P. Kingma,Max Welling +1 more
TL;DR: In this paper, a stochastic variational inference and learning algorithm was proposed for directed probabilistic models with intractable posterior distributions and large datasets, which scales to large datasets.
Proceedings Article
Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf,Max Welling +1 more
TL;DR: In this paper, a scalable approach for semi-supervised learning on graph-structured data is presented based on an efficient variant of convolutional neural networks which operate directly on graphs.
Book ChapterDOI
Modeling Relational Data with Graph Convolutional Networks
Michael Sejr Schlichtkrull,Thomas Kipf,Peter Bloem,Rianne van den Berg,Ivan Titov,Ivan Titov,Max Welling,Max Welling +7 more
TL;DR: It is shown that factorization models for link prediction such as DistMult can be significantly improved through the use of an R-GCN encoder model to accumulate evidence over multiple inference steps in the graph, demonstrating a large improvement of 29.8% on FB15k-237 over a decoder-only baseline.