15.1k citations · 36.9k across the 152 of their papers we have counts for
14 papers · 1 filter
Improved Bayesian Compression
Marco Federici, Karen Ullrich, Max Welling
Compression of Neural Networks (NN) has become a highly studied topic in recent years. The main reason for this is the demand for industrial scale usage of NNs such as deploying th…
Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification
Jakub M. Tomczak, Maximilian Ilse, Max Welling
The computer-aided analysis of medical scans is a longstanding goal in the medical imaging field. Currently, deep learning has became a dominant methodology for supporting patholog…
Learning Sparse Neural Networks through Regularization
Christos Louizos, Max Welling, Diederik P. Kingma
We propose a practical method for norm regularization for neural networks: pruning the network during training by encouraging weights to become exactly zero. Such regularizat…
Convolutional Networks for Spherical Signals
Taco Cohen, Mario Geiger, Jonas Köhler +1
The success of convolutional networks in learning problems involving planar signals such as images is due to their ability to exploit the translation symmetry of the data distribut…
Improving Variational Auto-Encoders using convex combination linear Inverse Autoregressive Flow
Jakub M. Tomczak, Max Welling
In this paper, we propose a new volume-preserving flow and show that it performs similarly to the linear general normalizing flow. The idea is to enrich a linear Inverse Autoregres…
Temporally Efficient Deep Learning with Spikes
Peter O'Connor, Efstratios Gavves, Max Welling
The vast majority of natural sensory data is temporally redundant. Video frames or audio samples which are sampled at nearby points in time tend to have similar values. Typically,…