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R. Emonet

12 papers hereh-index 211.6k citations106 works total

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

author position
  • middle author11
  • last author1

Across the 12 of 12 papers where every author was matched, so the position is known.

fields
  • cs.LG5
  • cs.CV2
  • cond-mat.mtrl-sci1
  • cs.CY1
  • cs.MA1
  • math.NA1
same name
  • R. Emonet — 2 papers, h 0

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
20162023
most citedTen simple rules for collaborative lesson development

16 citations · 59 across the 12 of their papers we have counts for

collaborators
Showing 2017Show all

4 papers · 1 filter

cs.CV2017★ 12 cited

Residual Conv-Deconv Grid Network for Semantic Segmentation

Damien Fourure, Rémi Emonet, Elisa Fromont +3

This paper presents GridNet, a new Convolutional Neural Network (CNN) architecture for semantic image segmentation (full scene labelling). Classical neural networks are implemented…

cs.CY2017★ 16 cited

Ten simple rules for collaborative lesson development

Gabriel A. Devenyi, Rémi Emonet, Rayna M. Harris +4

The collaborative development methods pioneered by the open source software community offer a way to create lessons that are open, accessible, and sustainable. This paper presents…

cs.MA2017

Improving Max-Sum through Decimation to Solve Loopy Distributed Constraint Optimization Problems

Jesús Cerquides, Rémi Emonet, Gauthier Picard +1

In the context of solving large distributed constraint optimization problems (DCOP), belief-propagation and approximate inference algorithms are candidates of choice. However, in g…

stat.ML2017★ 1 cited

L3-SVMs: Landmarks-based Linear Local Support Vectors Machines

Valentina Zantedeschi, Rémi Emonet, Marc Sebban

For their ability to capture non-linearities in the data and to scale to large training sets, local Support Vector Machines (SVMs) have received a special attention during the past…

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