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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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2024-10 Beyond top line metrics : understanding the trade-off between model size and generalization properties
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
2024-09 Exploring multivariate adaptations of the Lag-Llama univariate time series forecasting approach
2024-08 Enhancing agent learning through world dynamics modeling
2024-08 Learning representations for reasoning : generalizing across diverse structures
2024-08 Rule-based data augmentation for document-level medical concept extraction
2024-08 Parameter, experience, and compute efficient deep reinforcement learning
2024-08 On PI controllers for updating lagrange multipliers in constrained optimization
2024-07 Intrinsic exploration for reinforcement learning beyond rewards
2024-07 Performative prediction : expanding theoretical horizons
2024-07 Learning optimizers for communication-efficient distributed learning
2024-07 Domain adaptation in reinforcement learning via causal representation learning
2024-07 Advancing adversarial robustness with feature desensitization and synthesized data
2024-07 HarmonyCo : développement d'une bibliothèque Python pour le calcul optimisé de la médiane de permutations
2024-07 A LiDAR and Camera Based Convolutional Neural Network for the Real-Time Identification of Walking Terrain
2024-06 Mobility anomaly detection with intelligent video surveillance
2024-06 Promoting robustness and compositionality in machine learning with insights from cognitive bottlenecks
2024-06 Leveraging foundation models towards semantic world representations for robotics
2024-06 Distributed fog load balancing to support IoT applications : a reinforcement learning approach
2024-06 Constrained optimization for machine learning : algorithms and applications
2024-05 Strategic planning of intracity electric vehicle charging station locations with integrated advanced demand dynamics
2024-05 The shifting landscape of data : learning to tame distributional shifts
2024-05 Quotient Types in Typer
2024-05 Beyond the status quo in deep reinforcement learning
2024-04 Enhancing factuality and coverage in summarization via referencing key extracted content
2024-04 Generative models, theory and applications
2024-04 Scalable and robust fog-computing design & dimensioning in dynamic, trustless smart cities
2024-04 Searching for Q*
2024-03 Microservices identification in existing applications using meta-heuristics optimization and machine learning
2024-03 FACTS-ON : Fighting Against Counterfeit Truths in Online social Networks : fake news, misinformation and disinformation
2024-03 Sur la génération d'exemples pour réduire le coût d'annotation
2024-03 Self-supervision for reinforcement learning
2024-02 Génération de données synthétiques pour l'adaptation hors-domaine non-supervisée en réponse aux questions : méthodes basées sur des règles contre réseaux de neurones
2024-02 Evaluating approaches to solving proportional sentence analogies
2024-02 Metaheuristics for vehicle routing problems : new methods and performance analysis
2024-01 The role of continual learning and adaptive computation in improving computational efficiency of deep learning
2024-01 Beyond the horizon : improved long-range sequence modeling, from dynamical systems to language
2024-01 Dynamic capacities and priorities in stable matching
2023-12 Traitement automatique du langage naturel pour les textes juridiques : prédiction de verdict et exploitation de connaissances du domaine