Passer au contenu

/ Département d'informatique et de recherche opérationnelle

Je donne

Rechercher

Thèses et mémoires

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

 

 

Pour une recherche détaillée
Visiter Papyrus
Date Trier par date en ordre croissant Titre Trier par titre en ordre croissant
2025 Machine learning accelerated stochastic optimization and applications to railway operations
2025 Strategic capacity planning and pricing : a choice-based approach
2024 On PI controllers for updating lagrange multipliers in constrained optimization
2024 Promoting robustness and compositionality in machine learning with insights from cognitive bottlenecks
2024 Generative models, theory and applications
2024 A LiDAR and Camera Based Convolutional Neural Network for the Real-Time Identification of Walking Terrain
2024 Rule-based data augmentation for document-level medical concept extraction
2024 FACTS-ON : Fighting Against Counterfeit Truths in Online social Networks : fake news, misinformation and disinformation
2024 Scalable and robust fog-computing design & dimensioning in dynamic, trustless smart cities
2024 Advancing adversarial robustness with feature desensitization and synthesized data
2024 Dynamic capacities and priorities in stable matching
2024 Learning optimizers for communication-efficient distributed learning
2024 Sur la génération d'exemples pour réduire le coût d'annotation
2024 Domain adaptation in reinforcement learning via causal representation learning
2024 Parameter, experience, and compute efficient deep reinforcement learning
2024 Towards maintainable machine learning development through continual and modular learning
2024 Distributed fog load balancing to support IoT applications : a reinforcement learning approach
2024 Mobility anomaly detection with intelligent video surveillance
2024 Leveraging foundation models towards semantic world representations for robotics
2024 The shifting landscape of data : learning to tame distributional shifts