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Dustin Tran, edited by Chris Cremer & Chin-Wei Huang
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2026-03-08 21:53:27
raw text
Invertible Neural Nets and Normalizing Flows INNF+ 2021 ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models Home Schedule Call for Papers Author Instructions How It Works Accepted Papers Invited Speakers Organizers Chin-Wei Huang David Krueger Rianne van den Berg George Papamakarios Ricky Chen Danilo Rezende Overview Normalizing flows are explicit likelihood models that use invertible neural networks to construct flexible probability distributions of high-dimensional data. Compared to other generative models, the main advantage of normalizing flows is that they can offer 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 dens...
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