Deep learning relies on massive training sets of labeled examples to learn from - often tens of thousands to millions to reach peak predictive performance. However, large amounts of training data are only available for very few standardized learning problems. Even small variations of the problem specification or changes in the data distribution would necessitate re-annotation of large amounts of data.

However, domain knowledge can often be expressed by sets of prototypical descriptions. These knowledge-based descriptions can be either used as rule-based predictors or as labeling functions for providing partial data annotations. The growing field of weak supervision provides methods for refining and generalizing such heuristic-based annotations in interaction with deep neural networks and large amounts of unannotated data.

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. Learning with weak supervision is both studied from a theoretical perspective as well as applied to a variety of tasks from areas like natural language processing and computer vision. This workshop aims at bringing together researchers from this wide range of fields to facilitate discussions across research areas that share the common ground of using weak supervision. A target of this workshop is also to inspire applications of weak supervision to new scenarios and to enable researchers to work on tasks that so far have been considered too low-resource.

As weak supervision addresses one of the major issues of current machine learning techniques, the lack of labeled data, it has also started to obtain commercial interest. This workshop is an opportunity to bridge innovations from academia and the requirements of industry settings.