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Sylvester Normalizing Flows for Variational Inference
Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak +1
Variational inference relies on flexible approximate posterior distributions. Normalizing flows provide a general recipe to construct flexible variational posteriors. We introduce…
Graph Convolutional Matrix Completion
Rianne van den Berg, Thomas N. Kipf, Max Welling
We consider matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite…
Modeling Relational Data with Graph Convolutional Networks
Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem +3
Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. Despite the great effort invested in their creation and maintenance,…