268 citations · 1.4k across the 80 of their papers we have counts for
5 papers · 1 filter
The Separation Capacity of Random Neural Networks
Sjoerd Dirksen, Martin Genzel, Laurent Jacques +1
Neural networks with random weights appear in a variety of machine learning applications, most prominently as the initialization of many deep learning algorithms and as a computati…
When compressive learning fails: blame the decoder or the sketch?
Vincent Schellekens, Laurent Jacques
In compressive learning, a mixture model (a set of centroids or a Gaussian mixture) is learned from a sketch vector, that serves as a highly compressed representation of the datase…
Compressive Learning of Generative Networks
Vincent Schellekens, Laurent Jacques
Generative networks implicitly approximate complex densities from their sampling with impressive accuracy. However, because of the enormous scale of modern datasets, this training…
Compressive Classification (Machine Learning without learning)
Vincent Schellekens, Laurent Jacques
Compressive learning is a framework where (so far unsupervised) learning tasks use not the entire dataset but a compressed summary (sketch) of it. We propose a compressive learning…
Quantized Compressive K-Means
Vincent Schellekens, Laurent Jacques
The recent framework of compressive statistical learning aims at designing tractable learning algorithms that use only a heavily compressed representation-or sketch-of massive data…