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Autoregressive Diffusion Models

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If you have a question about this talk, please contact Elre Oldewage.

Autoregressive diffusion models are a new generative model that can be seen as an autoregressive model that places a distribution over variables in random dimension order. In this talk we will discuss their relation to standard (Gaussian-based) diffusion models, and the lessons (positive and negative) we can draw from this project.

Recommended reading: the original ‘modern’ diffusion paper (DDPMs) at https://arxiv.org/abs/2006.11239

Bio: Emiel is a last year PhD candidate at the University of Amsterdam under the supervision of Max Welling. Research interests include generative modelling and variational inference, for instance focusing on discrete variable extensions. He completed a BSc in Aerospace Engineering and a MSc in Artificial Intelligence. After a 3 month sabbatical he will join Google Brain in Amsterdam.

This talk is part of the Machine Learning Reading Group @ CUED series.

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