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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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2024 Intrinsic exploration for reinforcement learning beyond rewards
2024 HarmonyCo : développement d'une bibliothèque Python pour le calcul optimisé de la médiane de permutations
2024 Evaluating approaches to solving proportional sentence analogies
2024 Deep learning applications to climate change mitigation
2024 Searching for Q*
2024 On PI controllers for updating lagrange multipliers in constrained optimization
2024 Towards efficient large language models : training low-bitwidth variants and low-rank decomposition of pretrained models
2024 Enhancing risk-based authentication with federated learning : introducing the F-RBA framework
2024 Towards efficient and effective preference alignment for large language models
2024 Self-play for human-agent communication
2024 The shifting landscape of data : learning to tame distributional shifts
2024 Generative models, theory and applications
2024 Strategic planning of intracity electric vehicle charging station locations with integrated advanced demand dynamics
2024 Advancing adversarial robustness with feature desensitization and synthesized data
2024 Dichotomy(?) of fairness and efficiency
2024 Quotient Types in Typer
2024 Embedding cultural diversity in prototype-based recommender systems
2024 Learning optimizers for communication-efficient distributed learning
2024 Parameter, experience, and compute efficient deep reinforcement learning
2024 The role of continual learning and adaptive computation in improving computational efficiency of deep learning