Tutorial on Deep Generative Models (slides and video)

Slides for: Tutorial on Deep Generative Models by Shakir Mohamed and Danilo Rezende.


This tutorial will be a review of recent advances in deep generative models. Generative models have a long history at UAI and recent methods have combined the generality of probabilistic reasoning with the scalability of deep learning to develop learning algorithms that have been applied to a wide variety of problems giving state-of-the-art results in image generation, text-to-speech synthesis, and image captioning, amongst many others. Advances in deep generative models are at the forefront of deep learning research because of the promise they offer for allowing data-efficient learning, and for model-based reinforcement learning. At the end of this tutorial, audience member will have a full understanding of the latest advances in generative modelling covering three of the active types of models: Markov models, latent variable models and implicit models, and how these models can be scaled to high dimensional data. The tutorial will expose many questions that remain in this area, and for which thereremains a great deal of opportunity from members of the UAI community.

Deep sledding on the latest developments in deep generative models (August 2017 presentation) that ends with a bibliography starting on slide 84 of 96.

Depending on how much time has passed since the tutorial, try searching the topics as they are covered, keep a bibliography of your finds and compare it to that of the authors.

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