55 citations · 142 across the 3 of their papers we have counts for
Showing stat.MLShow all
2 papers · 1 filter
stat.ML2019★ 53 cited
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…
stat.ML2018★ 55 cited
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…