15.1k citations · 36.9k across the 152 of their papers we have counts for
16 papers · 1 filter
Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks
Raghavendra Selvan, Thomas Kipf, Max Welling +4
Graph refinement, or the task of obtaining subgraphs of interest from over-complete graphs, can have many varied applications. In this work, we extract trees or collection of sub-t…
Predictive Uncertainty through Quantization
Bastiaan S. Veeling, Rianne van den Berg, Max Welling
High-risk domains require reliable confidence estimates from predictive models. Deep latent variable models provide these, but suffer from the rigid variational distributions used…
Relaxed Quantization for Discretized Neural Networks
Christos Louizos, Matthias Reisser, Tijmen Blankevoort +2
Neural network quantization has become an important research area due to its great impact on deployment of large models on resource constrained devices. In order to train networks…
The Deep Weight Prior
Andrei Atanov, Arsenii Ashukha, Kirill Struminsky +2
Bayesian inference is known to provide a general framework for incorporating prior knowledge or specific properties into machine learning models via carefully choosing a prior dist…
Probabilistic Binary Neural Networks
Jorn W. T. Peters, Max Welling
Low bit-width weights and activations are an effective way of combating the increasing need for both memory and compute power of Deep Neural Networks. In this work, we present a pr…
Sample Efficient Semantic Segmentation using Rotation Equivariant Convolutional Networks
Jasper Linmans, Jim Winkens, Bastiaan S. Veeling +2
We propose a semantic segmentation model that exploits rotation and reflection symmetries. We demonstrate significant gains in sample efficiency due to increased weight sharing, as…