8.1k citations · 8.9k across the 3 of their papers we have counts for
3 papers
cs.DB2019
Estimating Cardinalities with Deep Sketches
Andreas Kipf, Dimitri Vorona, Jonas Müller +6
We introduce Deep Sketches, which are compact models of databases that allow us to estimate the result sizes of SQL queries. Deep Sketches are powered by a new deep learning approa…
stat.ML2016★ 891 cited
Variational Graph Auto-Encoders
Thomas N. Kipf, Max Welling
We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE). This model makes…
cs.LG2016★ 8.1k cited
Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf, Max Welling
We present 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 o…