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Thèses et mémoires

Des thèses et mémoires de nos étudiants sont conservés et consultables dans Papyrus, le dépôt institutionnel de l'Université de Montréal.

 

 

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2022 (Out-of-distribution?) : generalization in deep learning
2022 Renormalization group theory, scaling laws and deep learning
2024 Exploring multivariate adaptations of the Lag-Llama univariate time series forecasting approach
2022 Agent abstraction in multi-agent reinforcement learning
2023 Predicting stock market trends using time-series classification with dynamic neural networks
2021 Towards causal federated learning : a federated approach to learning representations using causal invariance
2026 Improving robustness and safety in multimodal AI systems
2023 Toward causal representation and structure learning
2022 Problem hierarchies in continual learning
2025 Learning under constraints
2023 Rethinking continual learning approach and study out-of-distribution generalization algorithms
2024 Towards efficient large language models : training low-bitwidth variants and low-rank decomposition of pretrained models
2024 Advancing adversarial robustness with feature desensitization and synthesized data
2023 Sur l’application de la structure de graphes pour le calcul automatique de nombres de reproduction dans les modèles à compartiments déterministes
2022 Generalization in federated learning
2023 Neurobiologically-inspired models : exploring behaviour prediction, learning algorithms, and reinforcement learning
2022 Parametric Scattering Networks
2025 AI in climate change adaptation : applications to remote sensing and climate science
1985 Le problème du choix de la séquence de classification des trains dans une gare de triage
1993 Tests d'indépendance de deux séries chronologiques multivariées
Number of results : 1662