203 citations · 461 across the 14 of their papers we have counts for
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stat.ML2021★ 8 cited
Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization
Léo Andeol, Yusei Kawakami, Yuichiro Wada +3
Domain shifts in the training data are common in practical applications of machine learning; they occur for instance when the data is coming from different sources. Ideally, a ML m…
stat.ML2016★ 92 cited
Investigating the influence of noise and distractors on the interpretation of neural networks
Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller +1
Understanding neural networks is becoming increasingly important. Over the last few years different types of visualisation and explanation methods have been proposed. However, none…