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INNF+: Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models
Chin-Wei Huang · David Krueger · Rianne Van den Berg · George Papamakarios · Ricky T. Q. Chen · Danilo J. Rezende

Fri Jul 23 02:28 AM -- 11:30 AM (PDT) @
Event URL: https://invertibleworkshop.github.io/ »

Normalizing flows are explicit likelihood models (ELM) characterized by a flexible invertible reparameterization of high-dimensional probability distributions. Unlike other ELMs, they offer both exact and efficient likelihood computation and data generation. Since their recent introduction, flow-based models have seen a significant resurgence of interest in the machine learning community. As a result, powerful flow-based models have been developed, with successes in density estimation, variational inference, and generative modeling of images, audio and video.

As the field is moving forward, the main goal of the workshop is to consolidate recent progress and connect ideas from related fields. Over the past few years, we’ve seen that normalizing flows are deeply connected to latent variable models, autoregressive models, and more recently, diffusion-based generative models. This year, we would like to further push the forefront of these explicit likelihood models through the lens of invertible reparameterization. We encourage researchers to use these models in conjunction to exploit the their benefits at once, and to work together to resolve some common issues of likelihood-based methods, such as mis-calibration of out-of-distribution uncertainty.

Author Information

Chin-Wei Huang (MILA)
David Krueger (Université de Montréal)
Rianne Van den Berg (University of Amsterdam)
George Papamakarios (DeepMind)
Ricky T. Q. Chen (U of Toronto)
Danilo J. Rezende (DeepMind)
Danilo J. Rezende

Danilo is a Senior Staff Research Scientist at Google DeepMind, where he works on probabilistic machine reasoning and learning algorithms. He has a BA in Physics and MSc in Theoretical Physics from Ecole Polytechnique (Palaiseau – France) and from the Institute of Theoretical Physics (SP – Brazil) and a Ph.D. in Computational Neuroscience at Ecole Polytechnique Federale de Lausanne, EPFL (Lausanne – Switzerland). His research focuses on scalable inference methods, generative models of complex data (such as images and video), applied probability, causal reasoning and unsupervised learning for decision-making.

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