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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…
Sketching Datasets for Large-Scale Learning (long version)
Rémi Gribonval, Antoine Chatalic, Nicolas Keriven +3
This article considers "compressive learning," an approach to large-scale machine learning where datasets are massively compressed before learning (e.g., clustering, classification…
Breaking the waves: asymmetric random periodic features for low-bitrate kernel machines
Vincent Schellekens, Laurent Jacques
Many signal processing and machine learning applications are built from evaluating a kernel on pairs of signals, e.g. to assess the similarity of an incoming query to a database of…
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…