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Alexander Lyzhov

2 accepted papers

2020

Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation

UAI 2020poster

Test-time data augmentation—averaging the predictions of a machine learning model across multiple augmented samples of data—is a widely used technique that improves the predictive performance. While many advanced learnable data augmentation techniques have emerged in recent years, they are focused o…

2020

Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning

ICLR 2020poster

Uncertainty estimation and ensembling methods go hand-in-hand. Uncertainty estimation is one of the main benchmarks for assessment of ensembling performance. At the same time, deep learning ensembles have provided state-of-the-art results in uncertainty estimation. In this work, we focus on in-domai…

Cited by 412SourceScholar