30 citations · 60 across the 5 of their papers we have counts for
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cs.CV2018★ 15 cited
Dropout Distillation for Efficiently Estimating Model Confidence
Corina Gurau, Alex Bewley, Ingmar Posner
We propose an efficient way to output better calibrated uncertainty scores from neural networks. The Distilled Dropout Network (DDN) makes standard (non-Bayesian) neural networks m…
stat.ML2018
Scrutinizing and De-Biasing Intuitive Physics with Neural Stethoscopes
Fabian B. Fuchs, Oliver Groth, Adam R. Kosiorek +4
Visually predicting the stability of block towers is a popular task in the domain of intuitive physics. While previous work focusses on prediction accuracy, a one-dimensional perfo…