Controllable music performance synthesis via hierarchical modelling

On discovering and learning structure under limited supervision

Syntactic inductive biases for deep learning methods

Advances in generative models for dynamic scenes

On iterated learning for task-oriented dialogue

Continuous coordination as a realistic scenario for lifelong learning

Towards deep unsupervised inverse graphics

Data-efficient reinforcement learning with self-predictive representations

On improving variational inference with low-variance multi-sample estimators

Estimation neuronale de l'information mutuelle.

Representation learning for dialogue systems

Deep learning and reinforcement learning methods for grounded goal-oriented dialogue

Emerging communication between competitive agents

Representation learning in unsupervised domain translation

Towards learning sentence representation with self-supervision

Advances in deep learning with limited supervision and computational resources

No Press Diplomacy

Latent variable language models

Sequence to sequence learning and its speech applications

Generative models : a critical review

Representation Learning for Visual Data

Learning visual representations with neural networks for video captioning and image generation

Exploring Attention Based Model for Captioning Images

Deep Learning for Video Modelling

Generative models for natural images

Speech synthesis using recurrent neural networks

Influencing the Properties of Latent Spaces

Sequential modeling, generative recurrent neural networks, and their applications to audio

Towards deep semi supervised learning

Deep learning of representations and its application to computer vision

Distributed conditional computation

Leveraging noisy side information for disentangling of factors of variation in a supervised setting

Canada Research Chair in Learning representations that generalize systematically

Software infrastructure for Deep Learning

Extending the Frontiers of Deep Generative Modelling

Fellow, CCAI Chair, CIFAR program in Learning in Machines & Brains

Physics-inspired Deep Generative Modeling and New Physics Search for High Energy Physics

Apogée Canada fonds d'excellence en recherche / Bourse d'excellence IVADO au doctorat Lluis E. Castrejon Subira

Support research related to the CIFAR program in Learning in Machines & Brains_Fellow

Visual-haptic Representation for Zero-shot Learning

Unsupervised Learning of 3D Scenes from Images using a View-based Representation

Fellow of the CIFAR LMB Program / CIFAR program Learning in Machine & Brains

Programme de stages - IVADO - Eugene Belilovsky /Towards Learning Language Based Navigation in Visually Rich 3-D Environments

Software infrastructure for Deep Learning

Unsupervised Learning of 3D Scenes from Images using a View-based Representation

Apprentissage non-supervisé d'architectures profondes pour le changement d'expressions faciales

Interative Grounded Language Understanding (IGLU)

CIFAR// Fellow // Research Support and Teaching Release // Learning in Machines & Brains

PROBABILISTIC MODELS FOR AUTOMATIC EQUIVARIANT FEATURE DISCOVERY

Highperformance computing environment to leverage deep learning technology for large biomedical and neuroimaging data

Deep Learning for Cognitive Computing

Interactive Grounded Language Understanding (IGLU)

RESEARCH ON SCALING UP DEEP LEARNING

DEVELOPPEMENT D'UN SYSTÈME ROBUSTE DE RECONNAISSANCE D'ACTIVITES HUMAINES UTILISANT DES FILMS ET LEUR TEXTE DESCRIPTIF

L'APPRENTISSAGE PROFOND DES IMAGES ET DU LANGAGE NATUREL

CIFAR//Fellow//Research Support

LARGE-SCALE DEEP LEARNING FOR CONTENT-BASED RECOMMENDATION SYSTEMS

ADIABATIC QUANTUM COMPUTING FOR DEEP LEARNING WITH BOLTZMANN MACHINES