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15

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Journal articles2 documents

  • Alejandro Lopez-Rincon, Alberto Tonda, Mohamed Elati, Olivier Schwander, Benjamin Piwowarski, et al.. Evolutionary optimization of convolutional neural networks for cancer miRNA biomarkers classification. Applied Soft Computing, Elsevier, 2018, 65, pp.91 - 100. ⟨10.1016/j.asoc.2017.12.036⟩. ⟨hal-01700622⟩
  • Adelene y L Sim, Olivier Schwander, Michael Levitt, Julie Bernauer. Evaluating mixture models for building RNA knowledge-based potentials.. Journal of Bioinformatics and Computational Biology, World Scientific Publishing, 2012, 10 (2), pp.1241010. ⟨10.1142/S0219720012410107⟩. ⟨hal-00757761⟩

Conference papers12 documents

  • Evangelos Moschos, Olivier Schwander, Alexandre Stegner, Patrick Gallinari. DEEP-SST-EDDIES: A Deep Learning framework to detect oceanic eddies in Sea Surface Temperature images. ICASSP 2020 - 45th International Conference on Acoustics, Speech, and Signal Processing, May 2020, Barcelona, Spain. ⟨hal-02470051⟩
  • Daniel Brooks, Olivier Schwander, Frédéric Barbaresco, Jean-Yves Schneider, Matthieu Cord. A Hermitian Positive Definite neural network for micro-Doppler complex covariance processing. International Radar Conference, Sep 2019, Toulon, France. ⟨hal-02422456⟩
  • Daniel Brooks, Olivier Schwander, Frédéric Barbaresco, Jean-Yves Schneider, Matthieu Cord. Riemannian batch normalization for SPD neural networks. Thirty-third Annual Conference on Neural Information Processing Systems., Dec 2019, Vancouver, Canada. ⟨hal-02422458⟩
  • Daniel Brooks, Olivier Schwander, Frédéric Barbaresco, Jean-Yves Schneider, Matthieu Cord. Complex-valued neural networks for fully-temporal micro-Doppler classification. 2019 20th International Radar Symposium (IRS), Jun 2019, Ulm, Germany. ⟨10.23919/IRS.2019.8768161⟩. ⟨hal-02290835⟩
  • Daniel Brooks, Olivier Schwander, Frédéric Barbaresco, Jean-Yves Schneider, Matthieu Cord. Exploring Complex Time-series Representations for Riemannian Machine Learning of Radar Data. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing, May 2019, Brighton, United Kingdom. pp.3672-3676, ⟨10.1109/ICASSP.2019.8683056⟩. ⟨hal-02290838⟩
  • Daniel Brooks, Olivier Schwander, Frédéric Barbaresco, Jean-Yves Schneider, Matthieu Cord. Second-order networks in PyTorch. GSI 2019 - 4th International Conference on Geometric Science of Information, Aug 2019, Toulouse, France. pp.751-758, ⟨10.1007/978-3-030-26980-7_78⟩. ⟨hal-02290841⟩
  • Tom Véniat, Olivier Schwander, Ludovic Denoyer. STOCHASTIC ADAPTIVE NEURAL ARCHITECTURE SEARCH FOR KEYWORD SPOTTING. ICASSP 2019 - International Conference on Acoustics, Speech, and Signal Processing, May 2019, Brighton, United Kingdom. ⟨hal-02063698⟩
  • Daniel Brooks, Olivier Schwander, Frédéric Barbaresco, Jean-Yves Schneider, Matthieu Cord. Temporal Deep Learning for Drone Micro-Doppler Classification. IRS 2018 - 19th International Radar Symposium, Jun 2018, Bonn, Germany. ⟨10.23919/IRS.2018.8447963⟩. ⟨hal-02290839⟩
  • Olivier Schwander, Stéphane Marchand-Maillet, Frank Nielsen. Comix: Joint Estimation and Lightspeed Comparison of Mixture Models. ICASSP 2016, 2016, Shanghai, China. ⟨10.1109/ICASSP.2016.7472117⟩. ⟨hal-01367923⟩
  • Olivier Schwander, Franck Nielsen. Bag-of-Components: An Online Algorithm for Batch Learning of Mixture Models. Geometric Science of Information GSI 2015, Oct 2015, Palaiseau, France. pp.387-395, ⟨10.1007/978-3-319-25040-3_42⟩. ⟨hal-01338652⟩
  • Olivier Schwander, José Picheral, Nicolas Gac, Ali-Mohammad Djafari, Daniel Blacodon. Aero-acoustics source separation with sparsity inducing priors in the frequency domain. 34th International Workshop on Bayesian Inference and Maximun Entropy Methods in Science and Engineering (MaxEnt'14), Sep 2014, Amboise, France. pp.422 - 431, ⟨10.1063/1.4906006⟩. ⟨hal-01103779⟩
  • Olivier Schwander, Frank Nielsen. Non-flat clustering whith alpha-divergences. ICASSP, May 2011, Prague, Czech Republic. pp.2100 - 2103, ⟨10.1109/ICASSP.2011.5946740⟩. ⟨hal-00708634⟩

Theses1 document

  • Olivier Schwander. Méthodes de géométrie de l'information pour les modèles de mélange. Apprentissage [cs.LG]. Ecole Polytechnique X, 2013. Français. ⟨pastel-00931722⟩