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This is a supporting wiki for the Perception&Learning group from Laboratoire Hubert Curien.
There is an associated mailing "firstname.lastname@example.org...".
You can contact "remi.emonet@..." for any question.
There are many synergies between artificial perception (vision, audio, image, ...) and machine learning (data mining, metric learning, ...): "machine learners" process more and more images and videos while "imagists" use and evolve learning methods. This group brings together the people interested in this convergence.
The list can be used to announce meetings and seminars, or to share information that can be of interest to registered people.
Events (add your ideas)
Ideas / Suggestions
- Metric learning in tracking (by Damien M.)
- Introduction to Topic Models (by Rémi)
- Domain adaptation by combining multiple classifiers (by Emilie)
- The C-Bound, its generalization to multiclass and multilabel, and its possible application to learning from multiple views (by Emilie)
- Introduction to Gaussian Process (kernel regression) (by Rémi)
- Introduction to Dirichlet Processes (bis) (by Rémi)
- Presentations by master interns
- Yimei : metric learning in DTW
- Tristan : 3d object generation (with Gaussian Processes?)
- Valentina : improved metric learning by combining local metrics
- 2016-03-03, 13h00, F021a: String representations and distances in deep convolutional neural networks for image classification (by Christophe Ducottet)
- 2016-02-11, 13h30-18h00, F021a/b: Afternoon On Sparsity
- Sparse matrices in code-based cryptography (by Pierre-Louis Cayrel) pdf
- Introduction to Sparsity in Modeling and Learning (by Rémi Emonet) pdf online
- Parsimony in Convex Optimization for Supervised Machine Learning (by Marc Sebban) pdf
- Sparsity in Probabilistic Models (by Rémi Emonet) pdf online
- Applications of sparsity in signal and image processing: from compressed sensing to sparse regularization (by Loïc Denis) pdf
- 2015-06-18, 14h00, F021b: Non rigid 3d registration (by Jorge Azorin) pdf1 pdf2
- 2015-05-21, 14h00, F021a: Introduction to Optimal Transport (by Michaël Perrot) pdf
- 2015-04-09, 14h00, F021a: extended round table discussion
- 2015-03-12, 14h00, F021b: Convolutional Neural Networks and Color Constancy (Damien Fourure)
- 2014-12-18, 13h00, F021b: Learning to Rank (Damien Muselet) pdf
- 2014-09-05 : Introduction to SVM (Marc Sebban) pdf