Lecture DL

Groupe de lecture deep learning

Session 12 : lundi 27 juin 2022, 11h00
- Aurelien Beaude : Hopular - Modern Hopfield Networks for Tabular Data
- Blaise Hanczar : Diffusion models
Session 11 : vendredi 25 mars 2022, 14h00 (visio collab)
- Désiré Sidibe : When Does Self-supervision Improve Few-shot Learning ?
- Alice Lacan : Autoencoder Image Interpolation by Shaping the Latent Space
Session 10 : vendredi 4 fevrier 2022, 13h 30(visio collab)
- Alexandre Heuillet : FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining
- Ayoub Abraich : Patches Are All You Need?
Session 9 : mercredi 10 novembre 2021, 14h (visio collab)
- Dominique Fourer : Improve the Interpretability of Attention: A Fast, Accurate, and Interpretable High-Resolution Attention Model, slides
- Victoria Bourgeais : Federated continual learning with weighted inter-client transfer, slides
Session 8 : mardi 21 septembre 2021, 11h (visio collab)
- Farida Zehraoui : Attention is not not Explanation
- Louis Becquey : Highly accurate protein structure prediction with AlphaFold
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

Intranet