Rémi Emonet's Research Home (see global home)
Selection of Recent Publications (see all publications and details)
- Year 2019WInterpreting Neural Networks as Majority Votes through the PAC-Bayesian Theory2019- NeurIPS 2019 Workshop on Machine Learning with Guarantees[bibtex] (click to show)
@inproceedings{viallard2019interpreting, title={Interpreting Neural Networks as Majority Votes through the PAC-Bayesian Theory}, author={Viallard, Paul and Emonet, R{\'e}mi and Germain, Pascal and Habrard, Amaury and Morvant, Emilie}, booktitle = {NeurIPS 2019 Workshop on Machine Learning with Guarantees}, year={2019} }
CAn Adjusted Nearest Neighbor Algorithm Maximizing the F-measure from Imbalanced Data2019- ICTAI (International Conference on Tools with Artificial Intelligence)[bibtex] (click to show)@inproceedings{metzler2019gnn, title={An Adjusted Nearest Neighbor Algorithm Maximizing the F-measure from Imbalanced Data}, author={ and Emonet, R{\'e}mi and Fromont, Elisa}, booktitle={IEEE International Conference on Image Processing}, year={2019} }
CFrom Cost-Sensitive Classification to Tight F-measure Bounds2019- AISTATS[bibtex] (click to show)@inproceedings{bascolmetzlerfmeasure, title={From Cost-Sensitive Classification to Tight F-measure Bounds}, author={Bascol, Kevin and Emonet, Rémi and Fromont, Élisa and Habrard, Amaury and Metzler, Guillaume and Sebban, Marc}, booktitle={AISTATS 2019}, year={2019} }
- Year 2018CFast and Provably Effective Multi-view Classification with Landmark-based SVM2018- ECML-PKDDhttps://github.com/vzantedeschi/multiviewLSVM[bibtex] (click to show)
@inproceedings{zantedeschi2018metric, title={ Fast and Provably Effective Multi-view Classification with Landmark-based SVM }, author={Zantedeschi, Valentina and Emonet, R{\'e}mi and Sebban, Marc}, booktitle={ECML-PKDD 2018}, year={2018} }
- Year 2017BUnsupervised Domain Adaptation Based on Subspace Alignment2017- Domain Adaptation in Computer Vision Applications[bibtex] (click to show)
@incollection{fernando2017unsupervised, title={Unsupervised Domain Adaptation Based on Subspace Alignment}, author={Fernando, Basura and Aljundi, Rahaf and Emonet, R{\'e}mi and Habrard, Amaury and Sebban, Marc and Tuytelaars, Tinne}, booktitle={Domain Adaptation in Computer Vision Applications}, pages={81--94}, year={2017}, publisher={Springer} } @book{csurka2017domain, title={Domain Adaptation in Computer Vision Applications}, author={Csurka, Gabriela}, year={2017}, publisher={Springer}, ISBN={978-3-319-58346-4}, DOI={10.1007/978-3-319-58347-1}, URL={http://www.springer.com/gp/book/9783319583464} }
CResidual Conv-Deconv Grid Network for Semantic Segmentation2017- BMVC (British Machine Vision Conference)https://github.com/Fourure/GridNet[bibtex] (click to show)@inproceedings{fourure2017gridnet, title={Residual Conv-Deconv Grid Network for Semantic Segmentation}, author={Fourure, Damien and Emonet, R{\'e}mi and Fromont, Elisa and Muselet, Damien and Tr{\'e}meau, Alain and Wolf, Christian}, booktitle={Proceedings of the British Machine Vision Conference, 2017}, year={2017} }
JMulti-task, Multi-domain Learning: application to semantic segmentation and pose regression2017- Neurocomputing[bibtex] (click to show)@article{fourure2017multi, title={Multi-task, Multi-domain Learning: application to semantic segmentation and pose regression}, author={Fourure, Damien and Emonet, R{\'e}mi and Fromont, Elisa and Muselet, Damien and Neverova, Natalia and Tr{\'e}meau, Alain and Wolf, Christian}, journal={Neurocomputing}, volume={251}, pages={68--80}, year={2017}, publisher={Elsevier} }
- Year 2016Cbeta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data2016- NIPS (Neural Information Processing Systems)http://vzantedeschi.com/betarisk.html[bibtex] (click to show)
@inproceedings{zantedeschi2016beta, title={beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data}, author={Zantedeschi, Valentina and Emonet, R{\'e}mi and Sebban, Marc}, booktitle={NIPS 2016}, year={2016} }
- Year 2015CLandmarks-based Kernelized Subspace Alignment for Unsupervised Domain Adaptation2015- CVPR (IEEE conf. Computer Vision and Pattern Recognition)[bibtex] (click to show)
@inproceedings{aljundi2015landmarks, title={Landmarks-based Kernelized Subspace Alignment for Unsupervised Domain Adaptation}, author={Aljundi, Rahaf and Emonet, R{\'e}mi and Muselet, Damien and Sebban, Marc}, booktitle={Computer Vision and Pattern Recognition}, year={2015} }
- Year 2014JTemporal Analysis of Motif Mixtures using Dirichlet Processes2014- TPAMI (Transactions on Pattern Analysis and Machine Intelligence)[bibtex] (click to show)
@ARTICLE{Emonet_PAMI_2014, author = {Emonet, Remi and Varadarajan, Jagannadan and Odobez, Jean-Marc}, title = {Temporal Analysis of Motif Mixtures using Dirichlet Processes}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, year = {2014} }
BSparsity in Topic Models2014- Practical Applications of Sparse Modeling: Biology, Signal Processing and Beyond[bibtex] (click to show)@INCOLLECTION {Varadarajan_MITPRESS_2012, title = {Sparsity in Topic Models}, author = {Varadarajan, Jagannadan and Emonet, Remi and Odobez, Jean-Marc}, booktitle = {Practical Applications of Sparse Modeling (Neural Information Processing series)}, year = {2014}, publisher = {MIT Press}, pdf = {https://publidiap.idiap.ch/downloads//papers/2012/Varadarajan_MITPRESS_2012.pdf}, } @book{9780262027724, Author = {}, Title = {Practical Applications of Sparse Modeling (Neural Information Processing series)}, Publisher = {The MIT Press}, Year = {2014}, ISBN = {0262027720}, URL = {http://www.amazon.com/Practical-Applications-Modeling-Information-Processing/dp/0262027720%3FSubscriptionId%3D0JYN1NVW651KCA56C102%26tag%3Dtechkie-20%26linkCode%3Dxm2%26camp%3D2025%26creative%3D165953%26creativeASIN%3D0262027720} }
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Demos
For now, visit my videos page.
Presentations (research and related)
Here are presentations that I made publicly enough so that it makes sense to share them. (click the icon on the left to see the sources and a PDF export)

















[2014-03-10] Temporal Topic Models for Probabilistic Motif Mining − SMiLe2014


Research Software and Related Projects





