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20/09/2013 @ 10h00 – 11h00 – Algorithmes d’apprentissage et bornes sur le risque pour l’approche de la régression à la prédiction de structures\nBy Mario Marchand\, Université Laval\, Québec\, Canada.\n\nNous présentons des garanties rigoureuses sur l’approche de la régression à la prédiction de sorties structurées. Nous montrons que le risque quadratique (de régression) borne supérieurement le risque de prédiction lorsque le noyau […]
11/07/2013 @ 14h00 – 15h00 – Comment interfacer séparation de sources et classification audio?\nBy Emmanuel Vincent\, Inria Nancy – Grand Est.\n\nOn considère le problème de la classification audio au sens large: reconnaissance de la parole\, identification du locuteur ou du chanteur\, etc. En situation réelle\, le signal cible est souvent superposé à d’autres signaux (bruit\, accompagnement musical\, etc). Si la séparation […]
04/07/2013 @ 14h00 – 15h00 – Spectral Learning of Hidden Structure or « How NLP (Natural Language Processing) is just a matter of compressive sensing. »\nBy Raphaël Bailly\, Universitat Politècnica de Catalunya.\n\nThe spectral algorithm\, as it has been introduced\, deals with sequences of observables symbols with a hidden sequence of states. In this talk\, we consider a supplementary hidden layer – e.g. a […]
27/06/2013 @ 15h00 – 16h00 – Critical order for moment estimation : insights from statistical physics.\nBy Florian Angeletti ENS Lyon\n\nAbstract:\nMoment estimation is one of the most basic question in statistical signal processing.\nFor i.i.d. random variables and a infinite number of observations\, the law of large numbers implies that the classical moment estimator converges towards the theoretical moment. However\, these hypothesis are […]
27/06/2013 @ 14h00 – 15h00 – Optimal Time-Frequency Bases for EEG Signal Classification in the Context of BCI. by Sebastiano Barbieri\n\nAbstract:\nWe consider the problem of classifying multi-sensor signals\, more\nprecisely EEG signals in the context of Brain Computer Interfaces (BCI)\, by selection of\ntime-frequency features. The features are determined among local cosine bases (MDCT)\nby a « best basis » type algorithm adapted to the classification […]
20/06/2013 @ 14h00 – 15h00 – Latticial Approach for clustering problems\nBy François Brucker\, Centrale Marseille\, LIF.\n\nWe present a combinatorial model which generalizes phylogenetic trees. This model links together a graph model (strongly chordal graphs)\, a lattice model (crown-free lattices) and a clustering model (chordal quasi-ultrametrics). This structure allows to model networks and to associate attributes/labels to data. In classification\, this kind […]
13/06/2013 @ 14h00 – 15h00 – Frames adapted to a time-frequency cover\n\nBy Jose Luis Romero \, NuHag\, Vienna\n\nGiven a possibly irregular cover of the time-frequency\nplane\, we construct dictionaries of atoms whose time-frequency profile\n(spectrogram) follows the given cover and show that these dictionaries\nprovide frame expansions. The aim of this construction is to have a\nframework for time-frequency analysis\, where the balance between time\nand […]
06/06/2013 @ 14h00 – 15h00 – Large scale network-guided feature selection in genome-wide association mapping. By Chloé-Agathe Azencott, Max Planck Institute. Genome-wide association studies (GWAS)\, in which hundreds of thousands\nor millions of single nucleotide polymorphisms (or SNPs) are genotyped\nfor up to tens of thousands of individuals\, are a powerful tool to\ndetect genetic loci likely to be associated with a complex trait. […]
06/06/2013 @ 10h00 – 11h00 – Robust image reconstruction from multi-view measurements.\nBy Pierre Vandergheynst\, EPFL.\n\nWe propose a novel method to accurately reconstruct a set of images representing a single scene from few linear multi-view measurements. Each observed image is modeled as the sum of a background image and a foreground one. The background image is common to all observed images but […]
30/05/2013 @ 14h00 – 15h00 – Solar Rotational Tomography: reconstruction of the electronic density in the Solar Corona.\n\nBy D. Vibert\, LAM\n\nAbstract:\nI will describe the concept of SRT applied to white light coronographic images\nand then focus on the time variation problem. The usual assumption is that the\ncorona is almost stable during one rotation so that a static tomography can be\nachieved. This is […]
