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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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2007 Modèles Pareto hybrides pour distributions asymétriques et à queues lourdes
2020 Advances in deep learning methods for speech recognition and understanding
2009 Sequential Machine learning Approaches for Portfolio Management
2018 Improved training of generative models
2020 On sample efficiency and systematic generalization of grounded language understanding with deep learning
2022 Latent data augmentation and modular structure for improved generalization
2024 Generative flow networks : theory and applications to structure learning
2018 Reparametrization in deep learning
2018 Sequence-to-sequence learning for machine translation and automatic differentiation for machine learning software tools
2020 Entity-centric representations in deep learning
2018 Applications of complex numbers to deep neural networks
2012 Apprentissage machine efficace : théorie et pratique
2018 Difference target propagation
2016 Bidirectional Helmholtz Machines
2020 Neural approaches to dialog modeling
2021 Towards computationally efficient neural networks with adaptive and dynamic computations
2003 Les algorithmes d'apprentissage appliqués aux risques financiers
2006 Collaborative filtering techniques for drug discovery
1999 Utilisation d'hyper-paramètres pour la sélection de variables
2011 Incorporating complex cells into neural networks for pattern classification
2009 Échantillonnage dynamique de champs markoviens
2015 Advances in scaling deep learning algorithms
2019 Deep neural networks for natural language processing and its acceleration
2013 Algorithmes d’apprentissage profonds supervisés et non-supervisés: applications et résultats théoriques
2022 Contributions to generative models and their applications
2018 Auto-Encoders, Distributed Training and Information Representation in Deep Neural Networks
2012 Algorithmes d'apprentissage pour la recommandation
2017 Feedforward deep architectures for classification and synthesis
2019 Learning competitive ensemble of information-constrained primitives
2024 Deep learning applications to climate change mitigation