Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL)
This workshop addresses the problem of reducing annotation costs for machine learning practitioners by leveraging weak supervision, but it is incremental as it builds on existing research in the field.
The workshop aimed to advance theory, methods, and tools for weakly supervised learning, focusing on using expert knowledge to annotate data for training deep neural networks, with 15 papers accepted for presentation.
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 experts to express prior coded knowledge for automatic data annotations that can be used to train arbitrary deep neural networks for prediction. The ICLR 2021 Workshop on Weak Supervision aims at advancing methods that help modern machine-learning methods to generalize from knowledge provided by experts, in interaction with observable (unlabeled) data. In total, 15 papers were accepted. All the accepted contributions are listed in these Proceedings.