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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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2024-07 A LiDAR and Camera Based Convolutional Neural Network for the Real-Time Identification of Walking Terrain
2024-07 Intrinsic exploration for reinforcement learning beyond rewards
2024-07 Advancing adversarial robustness with feature desensitization and synthesized data
2024-07 Learning optimizers for communication-efficient distributed learning
2024-07 Domain adaptation in reinforcement learning via causal representation learning
2024-08 Rule-based data augmentation for document-level medical concept extraction
2024-08 Parameter, experience, and compute efficient deep reinforcement learning
2024-08 Learning representations for reasoning : generalizing across diverse structures
2024-08 On PI controllers for updating lagrange multipliers in constrained optimization
2024-08 Enhancing agent learning through world dynamics modeling
2024-09 Exploring multivariate adaptations of the Lag-Llama univariate time series forecasting approach
2024-09 Aligning language models to code : exploring efficient, temporal, and preference alignment for code generation
2024-09 The equivalence of contrastive learning and graph convolution in collaborative filtering
2024-10 Beyond top line metrics : understanding the trade-off between model size and generalization properties