69 citations · 626 across the 60 of their papers we have counts for
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stat.ML2020★ 9 cited
Ambiguity in Sequential Data: Predicting Uncertain Futures with Recurrent Models
Alessandro Berlati, Oliver Scheel, Luigi Di Stefano +1
Ambiguity is inherently present in many machine learning tasks, but especially for sequential models seldom accounted for, as most only output a single prediction. In this work we…
stat.ML2020★ 54 cited
Restricting the Flow: Information Bottlenecks for Attribution
Karl Schulz, Leon Sixt, Federico Tombari +1
Attribution methods provide insights into the decision-making of machine learning models like artificial neural networks. For a given input sample, they assign a relevance score to…