Groupe de lecture deep learning

• Session 7 : jeudi 3 juin 2021, 15h (visio collab)
  • Vincent Vigneron : principal components , minor components and linear neural networks
  • Blaise Hanczar : Deep double descent : where bigger models and more data hurt
• Session 6 : 29 avril 2021 (visio collab)
  • Farida Zehraoui : Attention is not not Explanation
  • Tina Issa : Pareto multi-task learning
• Session 5 : 5 mars 2021 (visio collab)
  • Dominique Fourer : Adversarial poisonning and inverse poisonning against deep learning
  • Jean-Christophe Janodet : Distance and Equivalence between Finite State Machines and Recurrent Neural Networks: Computational results
• Session 4 : 18 décembre 2020 (visio collab)
  • Ayoub Abraich : Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models
  • Louis Becquey : Improved protein structure prediction using potentials from deep learning.(AlphaFold)
• Session 3 : 13 novembre 2020 (visio collab)
  • Victoria Bourgeais: Graph Neural Net intro & Graph U-nets (Gao, H., & Ji, S. 2019)
  • Tai Doan InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs (Shen et al. 2020)
• Session 2 : 29 septembre 2020 (12p)
  • Blaise Hanczar : The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
  • Tina Issa : PROXSGD training structured neural neworks under redularization and constraints
• Session 1 : 3 mars 2020 (10p)
  • Blaise Hanczar : Learning Representations for Counterfactual Inference
  • Farida Zehraoui : Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model

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