5 citations · 15 across the 14 of their papers we have counts for
5 papers · 1 filter
ExPLAIND: Unifying Model, Data, and Training Attribution to Study Model Behavior
Florian Eichin, Yupei Du, Philipp Mondorf +3
Post-hoc interpretability methods typically attribute a model's behavior to its components, data, or training trajectory in isolation, and are often tied to a particular level of g…
Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL)
Michael A. Hedderich, Benjamin Roth, Katharina Kann +3
Welcome to WeaSuL 2021, the First Workshop on Weakly Supervised Learning, co-located with ICLR 2021. In this workshop, we want to advance theory, methods and tools for allowing exp…
Analysing the Noise Model Error for Realistic Noisy Label Data
Michael A. Hedderich, Dawei Zhu, Dietrich Klakow
Distant and weak supervision allow to obtain large amounts of labeled training data quickly and cheaply, but these automatic annotations tend to contain a high amount of errors. A…
Learning Functions to Study the Benefit of Multitask Learning
Gabriele Bettgenhäuser, Michael A. Hedderich, Dietrich Klakow
We study and quantify the generalization patterns of multitask learning (MTL) models for sequence labeling tasks. MTL models are trained to optimize a set of related tasks jointly.…
Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data
Michael A. Hedderich, Dietrich Klakow
Manually labeled corpora are expensive to create and often not available for low-resource languages or domains. Automatic labeling approaches are an alternative way to obtain label…