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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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2025 Evaluating and improving mathematical reasoning in large language models via skill combinations
2025 Design and implementation of an AI-Driven educating bot for personalized science education in middle school students
2025 Enhancing electrophysiology imaging through artificial intelligence
2025 Towards more robust theoretical frameworks for deep neural network optimization
2025 Revisiting the security of quantum bit commitment schemes
2025 Segmentation automatisée des nerfs cornéens sous-basal utilisant l'apprentissage profond
2025 CLIP-Enhance : improving CLIP zero-shot classification via von Mises-Fisher clustering
2025 Competitive EV charging station location with queues
2025 Robust, efficient, and knowledge-augmented text generation with pre-trained language models
2025 CyberPRIcards : un jeu sérieux pour la sensibilisation à la vie privée et l’intimité numérique
2025 Towards adaptive personalization in conversational search
2025 Nouvelle contraction algébrique appliquée au problème de tarification de réseau
2025 Metacognitive architecture for perceptual and social systems : a neuro-inspired metacognition approach
2025 Machine learning accelerated stochastic optimization and applications to railway operations
2025 Classical and quantum decoding algorithms with ZX-Calculus
2025 Toward neural networks that generalize systematically
2025 Robustesse des réseaux neuronaux sur graphes
2025 Strategic capacity planning and pricing : a choice-based approach
2025 Investigating the impact of training data coverage on large language model hallucinations
2025 Learning under constraints
2025 Compromising creativity with feature modeling and metamodeling
2025 Data-driven large neighbourhood search for combinatorial optimization problems
2025 Designing scalable and efficient neural networks
2025 Vérification automatisée de programmes impératifs dans un langage à typage dépendant
2025 Rapid prototyping for systematic reuse in compartmental models
2025 Stereoscopic depth estimation with permutation
2025 Modeling the world to reason and plan
2025 Leveraging machine reading comprehension to mitigate hallucinations in answer generation by large language models
2025 Towards accurate RNA structural evaluation : a study of RNA tertiary structural evaluation and ARES performance improvement
2025 Multi-contrast image-to-image translation for axon and myelin segmentation
2025 Modélisation et analyse des données pour la simulation ferroviaire et la prévision des horaires en temps réel
2025 AI in climate change adaptation : applications to remote sensing and climate science
2024 Exploring multivariate adaptations of the Lag-Llama univariate time series forecasting approach
2024 Advancing adversarial robustness with feature desensitization and synthesized data
2024 Learning representations for reasoning : generalizing across diverse structures
2024 Towards maintainable machine learning development through continual and modular learning
2024 Performative prediction : expanding theoretical horizons
2024 Promoting robustness and compositionality in machine learning with insights from cognitive bottlenecks
2024 Dichotomy(?) of fairness and efficiency
2024 Constrained optimization for machine learning : algorithms and applications
2024 Self-play for human-agent communication
2024 Towards efficient large language models : training low-bitwidth variants and low-rank decomposition of pretrained models
2024 Towards human-AI co-creation for Hindustani music : modeling and interaction
2024 Searching for Q*
2024 Towards systematic generalization through meta-learning modular architectures and improving generative flow networks
2024 Beyond top line metrics : understanding the trade-off between model size and generalization properties
2024 Aligning language models to code : exploring efficient, temporal, and preference alignment for code generation
2024 Domain adaptation in reinforcement learning via causal representation learning
2024 An exploration of approximation chains
2024 Leveraging foundation models towards semantic world representations for robotics