91 citations · 104 across the 12 of their papers we have counts for
4 papers · 1 filter
SepLL: Separating Latent Class Labels from Weak Supervision Noise
Andreas Stephan, Vasiliki Kougia, Benjamin Roth
In the weakly supervised learning paradigm, labeling functions automatically assign heuristic, often noisy, labels to data samples. In this work, we provide a method for learning f…
KnowMAN: Weakly Supervised Multinomial Adversarial Networks
Luisa März, Ehsaneddin Asgari, Fabienne Braune +2
The absence of labeled data for training neural models is often addressed by leveraging knowledge about the specific task, resulting in heuristic but noisy labels. The knowledge is…
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…
Knodle: Modular Weakly Supervised Learning with PyTorch
Anastasiia Sedova, Andreas Stephan, Marina Speranskaya +1
Strategies for improving the training and prediction quality of weakly supervised machine learning models vary in how much they are tailored to a specific task or integrated with a…