34 citations · 34 across the 1 of their papers we have counts for
4 papers
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…
Improved Knowledge Distillation via Teacher Assistant
Seyed-Iman Mirzadeh, Mehrdad Farajtabar, Ang Li +3
Despite the fact that deep neural networks are powerful models and achieve appealing results on many tasks, they are too large to be deployed on edge devices like smartphones or em…
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…
Do Deep Generative Models Know What They Don't Know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh +2
A neural network deployed in the wild may be asked to make predictions for inputs that were drawn from a different distribution than that of the training data. A plethora of work h…