Detailed schedule

Session 1: Theoretical Machine Learning

  • Unlabeled sample compression schemes and corner peelings for ample and maximum classes
    Jérémie Chalopin (Equipe DALGO – Pôle Calcul)
  • Quantum Bandits
    Balthazar Casalé (Equipe CANA/QARMA – Pôle Calcul/SD)
  • Learning fast operators for machine learning
    Valentin Emiya (Equipe QARMA – Pôle SD)
  • Learning meaningful representations of life
    Paul Villoutreix (Equipe QARMA – Pôle SD)

Session 2: Applications of Machine Learning

  • Diagnosis and prognosis for fuel cell systems using machine learning tools
    Zhongliang Li (Equipe PECASE – Pôle ACS)
  • An Advanced Arrhythmia Recognition Methodology Based on R-waves Time-Series Derivatives and Benchmarking Machine-Learning Algorithms
    Youssef Trardi (Equipe PECASE – Pôle ACS)
  • Machine learning of human behaviour for human-machine interactions
    Magalie Ochs (Equipe R2I – Pôle SD)
  • IoT Data Imputation with Incremental Multiple Linear Regression
    Peng Tao (Equipe DIAMS – Pôle SD)

Session 3: Deep Learning

  • Deep Learning based Image Recognition
    Ronan Sicre (Equipe QARMA – Pôle SD)
  • Utilisation des dépendances dans la Classification relation issu de textes par apprentissage profond
    Sébastien Fournier (Equipe R2I – Pôle SD)
  • Neural representations of dialogical history improve upcoming turn acoustic parameters prediction
    Fuscone Simone (Equipe TALEP – Pôle SD)
  • Weakly Supervised Supersense Induction for French Nouns
    Alexis Nasr (Equipe TALEP – Pôle SD)