Modern image inpainting systems, despite the significant progress, often...
Averaging predictions over a set of models – an ensemble – is widely use...
Test-time data augmentation—averaging the predictions of a machine learn...
Uncertainty estimation and ensembling methods go hand-in-hand. Uncertain...
This paper proposes a semi-conditional normalizing flow model for
semi-s...
Bayesian inference is known to provide a general framework for incorpora...
In this paper, we propose variance networks, a new model that stores the...
In industrial machine learning pipelines, data often arrive in parts.
Pa...
In this work, we investigate Batch Normalization technique and propose i...
Dropout-based regularization methods can be regarded as injecting random...
We explore a recently proposed Variational Dropout technique that provid...