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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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Date Trier par date en ordre croissant Titre Trier par titre en ordre croissant
2017 Deep Learning for Video Modelling
2023 Emergence of language-like latents in deep neural networks
2019 No Press Diplomacy
2023 Sequential decision modeling in uncertain conditions
2021 AI alignment and generalization in deep learning
2018 Generative models : a critical review
2024 Promoting robustness and compositionality in machine learning with insights from cognitive bottlenecks
2018 Latent variable language models
2019 Emerging communication between competitive agents
2019 Representation learning in unsupervised domain translation
2017 Generative models for natural images
2016 Influencing the Properties of Latent Spaces
2020 Deep learning and reinforcement learning methods for grounded goal-oriented dialogue
2020 Estimation neuronale de l'information mutuelle
2019 Towards learning sentence representation with self-supervision
2023 Probability flows in deep learning
2022 Syntactic inductive biases for deep learning methods
2023 Coordination in generative modeling, automatic differentiation and multi-agent learning
2018 Advances in deep learning with limited supervision and computational resources
2020 On improving variational inference with low-variance multi-sample estimators
2018 Sequence to sequence learning and its speech applications
2022 On discovering and learning structure under limited supervision
2023 Improving predictive behavior under distributional shift
2021 Continuous coordination as a realistic scenario for lifelong learning
2022 Advances in generative models for dynamic scenes
2016 Towards deep semi supervised learning
2019 Representation learning for dialogue systems
2016 Speech synthesis using recurrent neural networks
2024 Beyond the status quo in deep reinforcement learning
2024 Beyond top line metrics : understanding the trade-off between model size and generalization properties