Tuesday 4 September
(13h Reception opens)
14h Keynote 1: Stéphane Mallat (ENS Paris / Collège de France) "Unsupervised learning: from maximum entropy to deep generative networks"
15h Session on Maths and Signal

• François Malgouyres (Institut de Mathématiques de Toulouse, Univ. Paul Sabatier): “Multilinear compressive sensing and an application to convolutional linear networks”
• Nicolas Courty (Univ. Bretagne Sud / IRISA):”Optimal Transport and Domain Adaptation”
• Vincent Gripon (IMT Atlantique): “Convolutional Neural Networks for Signals on Graphs"
• Michel Sebag (LRI): "Searching Causal Models" 16h30 Coffee break
  17h Panel on scientific challenges, organized by Gérard Biau (Sorbonne Université)
  18h END

Wednesday 5 September
(8h30 Reception opens)
9h Keynote 2: Pierre Vandergheynst (EPFL) Geometric deep learning and applications
10h Coffee break
10h30 Session on Mathematical Tools

• Johannes Schmidt-Hieber (Leiden University): "Statistical theory for deep ReLU networks"
• Gerardo Rubino (Inria): "Machine Learning with Random Neural Networks''
• Antoine Chatalic (Univ. Rennes 1): "Compressive learning with random moments"
• Erwan Le Merrer (Tecnicolor): “Decision boundaries & security related questions”
  12h Lunch
  13h30 Keynote 3: Cordelia Schmid (Inria) "Automatic understanding of the visual world"
  14h30 Session on Audio
• Romain Hennequin (Deezer): “Audio-based metric learning for artist disambiguation in large music catalogs”
• Fabian-Robert Stöter (Inria & LIRMM / Univ Montpellier): “Deep Learning for Music Unmixing”
• Sanjeel Parekh (Telecom ParisTech / Technicolor): “Representation learning for audio-visual events”
• Laurent Girin (Univ. Grenoble Alpes, Grenoble-INP, GIPSA-lab): “Deep learning for speech enhancement”
  16h Coffee Break
  16h30 Keynote 4: François Pachet (Spotify) "Controling generative models for music generation: from Markov chains to deep networks"
  17h30 END

Thursday 6 September
(8h30 Reception opens)
9h Keynote 5: Matthieu Cord (Sorbonne Université) "Visual Question Answering: A new task for vision and language understanding"
10h Coffee Break
10h30 Session on Images

• Martin Engilberge (Sorbonne Université / Technicolor): “Deep semantic-visual embedding with localization”
• Elisa Fromont (Univ. Rennes 1): "Deep learning from imbalanced data"
• Gilles Puy (Technicolor): “A flexible convolutional solver with application to photorealistic style transfer”
• Thierry Dumas (Inria): “Deep learning for intra picture prediction”
  12h Lunch
  13h30 Keynote 6: Olivier Pietquin (Google Brain): “Deep reinforcement learning with demonstrations”
  14h30 Session on Language
• Julien Perez (Naver Labs Europe): “Machine Reading, recent advances”
• Loic Barrault (LIUM, Le Mans Université): "Multimodal machine translation”
  15h20 Farewell coffee
  END