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Publications: [@varadarajan_probabilistic_2010] [@varadarajan_sparsity_2010] [@emonet_extracting_2011] [@emonet_multi-camera_2011] [@noauthor_intelligent_2012] [@noauthor_outils_2012] [@odobez_unsupervised_2012] [@emonet_automatic_2012] [@varadarajan_bridging_2012] [@aubert_clustering_2013] [@aubert_discovering_2013] [@chockalingam_localized_2013] [@tavenard_time-sensitive_2013] [@varadarajan_sequential_2013] [@emonet_temporal_2014] [@emonet_what_2014] [@noauthor_practical_2014] [@varadarajan_sparsity_2014] [@bascol_unsupervised_2016] [@mohiuddin_gpu_2018] [@ruswurm_end2end_2019] [@wang_classification_2019] [@wang_learning_2019] [@wang_adversarial_2020] [@ruswurm_endtoend_2023].

Projets : VANAHEIM, ROIi.

Codes et liens divers

- Site web relatif aux travaux de postdoc, avec explications et accès au programmes et articles. \
  [https://www.idiap.ch/resource/research/probamod-v1/](https://www.idiap.ch/resource/research/probamod-v1/){target="_blank"}
- Site web pour l'article CVPR 2011 [@emonet_extracting_2011]. \
  [https://home.heeere.com/publi-2011-cvpr.html](https://home.heeere.com/publi-2011-cvpr.html){target="_blank"}
- Supplementary material pour l'article CVPR 2011 [@emonet_extracting_2011]. \
  [https://www.idiap.ch/paper/2053/](https://www.idiap.ch/paper/2053/){target="_blank"}
- Code de la ré-implémentation en python du model HDLSM par %%CV-people.RomainTavenard.firstname%% %%CV-people.RomainTavenard.lastname%%. \
  [https://github.com/twitwi/hdlsm](https://github.com/twitwi/hdlsm){target="_blank"}
- Code pour les travaux à base d'auto-encodeurs. \
  [https://github.com/twitwi/Unsupervised-Interpretable-Pattern-Discovery-in-Time-Series-Using-Autoencoders](https://github.com/twitwi/Unsupervised-Interpretable-Pattern-Discovery-in-Time-Series-Using-Autoencoders){target="_blank"}

Présentations

- Unsupervised Decomposition of Images into Motifs \
  [https://dl.heeere.com/2022-06-16-export-fouille-motifs.pdf](https://dl.heeere.com/2022-06-16-export-fouille-motifs.pdf){target="_blank"}
- A Tour of Probabilistic and Deep Approaches for Unsupervised Learning \
  [http://twitwi.github.io/Presentation-2016-12-13-Journee-Ml-Opt/](http://twitwi.github.io/Presentation-2016-12-13-Journee-Ml-Opt/){target="_blank"}
- Infinite Mixture Models with Dirichlet Process \
  [https://github.com/twitwi/Presentation-2015-dirichlet-processes/](https://github.com/twitwi/Presentation-2015-dirichlet-processes/){target="_blank"}
- Temporal Topic Models for Probabilistic Motif Mining \
  [http://www.slideshare.net/remiemonet/temporal-topic-models-for-probabilistic-motif-mining-smile2014](http://www.slideshare.net/remiemonet/temporal-topic-models-for-probabilistic-motif-mining-smile2014){target="_blank"}

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