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Laurent Jacques

INMA, ICTEAM, UCLouvain

62 papers hereh-index 304.3k citations194 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author2
  • first author3
  • middle author19
  • last author35

Across the 59 of 62 papers where every author was matched, so the position is known.

fields
  • eess.SP17
  • cs.CV13
  • cs.IT13
  • astro-ph.IM5
  • astro-ph4
  • cs.LG4
affiliations
  • INMA, ICTEAM, UCLouvain
HomepageORCID 0000-0002-6261-0328
same name
  • Laurent Jacques — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20052025
most citedCompressed sensing imaging techniques for radio interferometry

268 citations · 591 across the 38 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2020

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…

cs.LG2020

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…

cs.LG2018

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…

cs.LG2018

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.