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cs.LG2020★ 24 cited
Perfect density models cannot guarantee anomaly detection
Charline Le Lan, Laurent Dinh
Thanks to the tractability of their likelihood, several deep generative models show promise for seemingly straightforward but important applications like anomaly detection, uncerta…
cs.LG2020★ 23 cited
Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models
Chin-Wei Huang, Laurent Dinh, Aaron Courville
In this work, we propose a new family of generative flows on an augmented data space, with an aim to improve expressivity without drastically increasing the computational cost of s…