124 citations · 499 across the 26 of their papers we have counts for
3 papers · 1 filter
Learning Likelihoods with Conditional Normalizing Flows
Christina Winkler, Daniel Worrall, Emiel Hoogeboom +1
Normalizing Flows (NFs) are able to model complicated distributions p(y) with strong inter-dimensional correlations and high multimodality by transforming a simple base density p(z…
Integer Discrete Flows and Lossless Compression
Emiel Hoogeboom, Jorn W. T. Peters, Rianne van den Berg +1
Lossless compression methods shorten the expected representation size of data without loss of information, using a statistical model. Flow-based models are attractive in this setti…
Emerging Convolutions for Generative Normalizing Flows
Emiel Hoogeboom, Rianne van den Berg, Max Welling
Generative flows are attractive because they admit exact likelihood optimization and efficient image synthesis. Recently, Kingma & Dhariwal (2018) demonstrated with Glow that gener…