Olivier Schwander

发表

Deep-SST-Eddies: A Deep Learning Framework to Detect Oceanic Eddies in Sea Surface Temperature Images

Patrick Gallinari, Evangelos Moschos, Olivier Schwander, 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Non-flat clustering with alpha-divergences

Frank Nielsen, Olivier Schwander, F. Nielsen, 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Complex-valued neural networks for fully-temporal micro-Doppler classification

Matthieu Cord, Jean-Yves Schneider, Frédéric Barbaresco, 2019, 2019 20th International Radar Symposium (IRS).

Reranking with Contextual Dissimilarity Measures from Representational Bregman k-Means

Frank Nielsen, Olivier Schwander, F. Nielsen, 2010, VISAPP.

Exploring Complex Time-series Representations for Riemannian Machine Learning of Radar Data

Matthieu Cord, Frederic Barbaresco, Jean-Yves Schneider, 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

k-MLE for mixtures of generalized Gaussians

Frank Nielsen, Yannick Berthoumieu, Aurelien J. Schutz, 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

Stochastic Adaptive Neural Architecture Search for Keyword Spotting

Ludovic Denoyer, Olivier Schwander, Tom Véniat, 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Reduced Basis’ Acquisition by a Learning Process for Rapid On-line Approximation of Solution to PDE’s: Laminar Flow Past a Backstep

Patrick Gallinari, Yvon Maday, Maxime Sangnier, 2017 .

Temporal Deep Learning for Drone Micro-Doppler Classification

Matthieu Cord, Frederic Barbaresco, Jean-Yves Schneider, 2018, 2018 19th International Radar Symposium (IRS).

Bhattacharyya Clustering with Applications to Mixture Simplifications

Frank Nielsen, Olivier Schwander, Sylvain Boltz, 2010, 2010 20th International Conference on Pattern Recognition.

Learning Mixtures by Simplifying Kernel Density Estimators

Frank Nielsen, Olivier Schwander, F. Nielsen, 2013 .

Comix: Joint estimation and lightspeed comparison of mixture models

Frank Nielsen, Stéphane Marchand-Maillet, Olivier Schwander, 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Deep Learning and Information Geometry for Drone Micro-Doppler Radar Classification

Matthieu Cord, Frédéric Barbaresco, Olivier Schwander, 2020, 2020 IEEE Radar Conference (RadarConf20).

Learning and Recognition in Vision

Cordelia Schmid, Christophe Damerval, Joost van de Weijer, 2006 .

Bag-of-Components: An Online Algorithm for Batch Learning of Mixture Models

Frank Nielsen, Olivier Schwander, F. Nielsen, 2015, GSI.

Evaluating Mixture Models for Building RNA Knowledge-Based Potentials

Olivier Schwander, Julie Bernauer, Adelene Y. L. Sim, 2012, J. Bioinform. Comput. Biol..

Model centroids for the simplification of Kernel Density estimators

Frank Nielsen, Olivier Schwander, 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Classification of Eddy Sea Surface Temperature Signatures Under Cloud Coverage

Patrick Gallinari, Evangelos Moschos, Olivier Schwander, 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

Code You Are Happy to Paste: An Algorithmic Dictionary of Exponential Families

Olivier Schwander, 2014, ECML/PKDD.

Second-Order Networks in PyTorch

Matthieu Cord, Jean-Yves Schneider, Frédéric Barbaresco, 2019, GSI.

Evolutionary optimization of convolutional neural networks for cancer miRNA biomarkers classification

Benjamin Piwowarski, Patrick Gallinari, Alejandro Lopez Rincon, 2018, Appl. Soft Comput..

Riemannian batch normalization for SPD neural networks

Matthieu Cord, Jean-Yves Schneider, Olivier Schwander, 2019, NeurIPS.

Méthodes de géométrie de l'information pour les modèles de mélange. (Information-geometric methods for mixture models)

Olivier Schwander, Olivier Schwander, 2013 .

Fast Learning of Gamma Mixture Models with k-MLE

Frank Nielsen, Olivier Schwander, F. Nielsen, 2013, SIMBAD.

PyMEF — A framework for exponential families in Python

Frank Nielsen, Olivier Schwander, 2011, 2011 IEEE Statistical Signal Processing Workshop (SSP).

Stochatic Adaptive Neural Architecture Search

Ludovic Denoyer, Olivier Schwander, Tom Veniat, 2019 .

Aero-acoustics source separation with sparsity inducing priors in the frequency domain

Ali Mohammad-Djafari, José Picheral, Olivier Schwander, 2015 .

Exploring deep vision models for acoustic scene classification

Matthieu Cord, Olivier Schwander, Octave Mariotti, 2018, DCASE.