15.1k citations · 36.9k across the 152 of their papers we have counts for
16 papers · 1 filter
Steerable CNNs
Taco S. Cohen, Max Welling
It has long been recognized that the invariance and equivariance properties of a representation are critically important for success in many vision tasks. In this paper we present…
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…
Sigma Delta Quantized Networks
Peter O'Connor, Max Welling
Deep neural networks can be obscenely wasteful. When processing video, a convolutional network expends a fixed amount of computation for each frame with no regard to the similarity…
Accelerating the BSM interpretation of LHC data with machine learning
Gianfranco Bertone, Marc Peter Deisenroth, Jong Soo Kim +3
The interpretation of Large Hadron Collider (LHC) data in the framework of Beyond the Standard Model (BSM) theories is hampered by the need to run computationally expensive event g…
Improving Variational Auto-Encoders using Householder Flow
Jakub M. Tomczak, Max Welling
Variational auto-encoders (VAE) are scalable and powerful generative models. However, the choice of the variational posterior determines tractability and flexibility of the VAE. Co…
Variational Bayes In Private Settings (VIPS)
Mijung Park, James Foulds, Kamalika Chaudhuri +1
Many applications of Bayesian data analysis involve sensitive information, motivating methods which ensure that privacy is protected. We introduce a general privacy-preserving fram…