Rémi Emonet's Research Home     (see global home)

Selection of publications     (see all publications and details)

  • Year 2022
    C
    Optimal Tensor Transport
    2022
    - AAAI (AAAI Conference on Artificial Intelligence)
    [gs]
    [bibtex] (click to show)
    @inproceedings{kerdoncuff2022optimal,
        title={Optimal Tensor Transport},
        author={Kerdoncuff, Tanguy and Perrot, Micha{\"e}l and Emonet, R{\'e}mi and Sebban, Marc},
        booktitle={Proceedings (AAAI Artificial Intelligence Conference)},
        year={2022}
    }
    
    pub87
  • Year 2021
    C
    Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound
    2021
    - NeurIPS (Advances in Neural Information Processing Systems)
    [gs]
    [bibtex] (click to show)
    @article{zantedeschi2021learning,
        title={Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound},
        author={Zantedeschi, Valentina and Viallard, Paul and Morvant, Emilie and Emonet, R{\'e}mi and Habrard, Amaury and Germain, Pascal and Guedj, Benjamin},
        journal={Advances in Neural Information Processing Systems},
        volume={34},
        year={2021}
    }
    
    pub84
  • Year 2019
    C
    An Adjusted Nearest Neighbor Algorithm Maximizing the F-measure from Imbalanced Data
    2019
    - ICTAI (International Conference on Tools with Artificial Intelligence)
    [gs]
    [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}
    }
    
    pub071
    C
    From Cost-Sensitive to Tight F-measure Bounds
    2019
    - AISTATS (International Conference on Artificial Intelligence and Statistics)
    [gs]
    [bibtex] (click to show)
    @inproceedings{bascolmetzlerfmeasure,
      title={From Cost-Sensitive to Tight F-measure Bounds},
      author={Bascol, Kevin and Emonet, Rémi and Fromont, Elisa and Habrard, Amaury and Metzler, Guillaume and Sebban, Marc},
      booktitle={AISTATS 2019},
      year={2019},
      comment={CONE},
    }
    
    pub063
  • Year 2017
    C
    Residual Conv-Deconv Grid Network for Semantic Segmentation
    2017
    - BMVC (British Machine Vision Conference)
    https://github.com/Fourure/GridNet[gs]
    [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}
    }
    
    pub053
    J
    Multi-task, Multi-domain Learning: application to semantic segmentation and pose regression
    2017
    - Neurocomputing
    [gs]
    [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}
    }
    
    pub052
  • Year 2015
    C
    Landmarks-based Kernelized Subspace Alignment for Unsupervised Domain Adaptation
    2015
    - CVPR (IEEE conf. Computer Vision and Pattern Recognition)
    [gs]
    [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}
    }
    
    pub041
  • Year 2014
    J
    Temporal Analysis of Motif Mixtures using Dirichlet Processes
    2014
    - TPAMI (Transactions on Pattern Analysis and Machine Intelligence)
    [gs]
    [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}
    }
    
    pub030
  • Year
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
    -
    [gs]
  • [C]Optimal Tensor Transport
    Tanguy Kerdoncuff, Michaël Perrot, Rémi Emonet, Marc Sebban
    2022Core A*AAAI (AAAI Conference on Artificial Intelligence)
  • [C]Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound
    Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj
    2021Core A*NeurIPS (Advances in Neural Information Processing Systems)
  • [C]An Adjusted Nearest Neighbor Algorithm Maximizing the F-measure from Imbalanced Data
    Rémi Viola, Rémi Emonet, Amaury Habrard, Guillaume Metzler, Stéphane Riou, Marc Sebban
    2019Core BICTAI (International Conference on Tools with Artificial Intelligence)
  • [C]From Cost-Sensitive to Tight F-measure Bounds
    Kevin Bascol, Rémi Emonet, Élisa Fromont, Amaury Habrard, Guillaume Metzler, Marc Sebban
    2019Core AAISTATS (International Conference on Artificial Intelligence and Statistics)
  • [C]Residual Conv-Deconv Grid Network for Semantic Segmentation
    Damien Fourure, Rémi Emonet, Élisa Fromont, Damien Muselet, Alain Trémeau, Christian Wolf
    2017Core BBMVC (British Machine Vision Conference)
  • [J]Multi-task, Multi-domain Learning: application to semantic segmentation and pose regression
    Damien Fourure, Rémi Emonet, Élisa Fromont, Damien Muselet, Natalia Neverova, Alain Trémeau, Christian Wolf
    2017Q1IF 3.317Neurocomputing
  • [C]Landmarks-based Kernelized Subspace Alignment for Unsupervised Domain Adaptation
    Rahaf Aljundi, Rémi Emonet, Damien Muselet, Marc Sebban
    2015Core ACVPR (IEEE conf. Computer Vision and Pattern Recognition)
  • [J]Temporal Analysis of Motif Mixtures using Dirichlet Processes
    Rémi Emonet, Jagannadan Varadarajan, Jean-Marc Odobez
    2014Q1IF 5.694TPAMI (Transactions on Pattern Analysis and Machine Intelligence)

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)

[2022-06-16]  Unsupervised Decomposition of Images into Motifs (GDR Appamat)
[2021-11-18]  Deep Equilibrium Models and Implicit Layers (LabHC)
[2021-11-16]  Optimal Transport Based RNN Distillation (ANR TAUDoS)
[2021-07-06]  Learning from Imbalanced Data and Anomaly Detection (EUR Sleight SSE6)
gh[2021-04-21]  Bayesian Neural Networks - Uncertainty Quantification (Deep Imaging Summer School)
gh[2020-01-23]  Défi IA − Anomaly Detection: class imbalance or novelty
[2019-06-27]  Behavior of Distance-Based Methods (LACODAM Team)
gh[2019-06-13]  Dealing with Imbalanced Data, and, Interpretability via Adversarial Regularization (NAVER Labs)
[2018-06-07]  A Tour of Machine Learning and its Subdomains (LabHC)
gh[2018-06-06]  Domain Adaptation and Multi-view Learning: using subspace alignment and landmark projections
gh[2017-04-22]  Deep Learning pour la Reconnaissance de Chatons
gh[2017-01-20]  Version control with Git at CIRAD − 2017
gh[2017-01-04]  Likelihood-based and Likelihood-free Unsupervised Learning
gh[2016-12-13]  A Tour of Probabilistic and Deep Approaches for Unsupervised Learning − Optim. and ML Day
gh[2016-09-01]  Introduction to Version Control using Git and Gitlab − 2016
gh[2016-05-25]  Learn to Learn: Facts About Learning − Web En Vert
gh[2016-02-11]  About Sparsity and Sparsity in Probabilistic Modeling
gh[2015-11-27]  Unsupervised Domain Adaptation, Deep CNNs − Xerox Research Center Europe
gh[2015-11-26]  Deep Learning Frameworks − Saintélyon Deep Learning Workshop
gh[2015-09-30]  Machine Learning Introduction − Web En Vert
gh[2015-04-30]  Dirichlet Processes for GMM − Image&Tracking group
gh[2014-06-20]  Fisher Kernel and Fisher Vectors − ANR SoLSTiCe project
gh[2014-05-15]  Probabilistic Views of Classical Problems − About probabilities and prior
[2014-03-10] Temporal Topic Models for Probabilistic Motif Mining − SMiLe2014
gh[2014-10-23]  Introduction to Version Control using Git and Gitlab − 2014
gh[2013-10-03]  Introduction to Version Control using Git and Gitlab − 2013

Research Software and Related Projects

See also individual links in my publication list, or a list of paper-related github repositories.

Temporal probabilistic models website. Implementation of EM-style and non-parametric models (Gibbs sampling). Sharing code from our work done mostly while at the Idiap research institute.
Human detection source code, used in the VANAHEIM project. I mostly worked on the finalization, cleanup and packaging, and on the website.
The framework I use and improve to build my teaching and research slides. (I now moved to other frameworks and to https://sli.dev/)
A modular framework to make executables by assembling modules in Java, C++ and Python. Created and used during my stay in INRIA-PRIMA and at Idiap (in the VANAHEIM project).
A Gaussian pyramid processor using kernels in OpenCL with C and Java interfaces. It implements the square-rooted version which does less processing.
A service oriented middleware for ambient intelligence and pervasive computing. Works across operating systems and programming languages. Developed in INRIA-PRIMA at the time of my Ph.D. Thesis.