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)