345 citations · 487 across the 9 of their papers we have counts for
3 papers · 1 filter
Normalizing Flows for Probabilistic Modeling and Inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende +2
Normalizing flows provide a general mechanism for defining expressive probability distributions, only requiring the specification of a (usually simple) base distribution and a seri…
Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh +1
Recent work has shown that deep generative models can assign higher likelihood to out-of-distribution data sets than to their training data (Nalisnick et al., 2019; Choi et al., 20…
Hybrid Models with Deep and Invertible Features
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh +2
We propose a neural hybrid model consisting of a linear model defined on a set of features computed by a deep, invertible transformation (i.e. a normalizing flow). An attractive pr…