This is a one day workshop taking place in-person in Kassel.
More details and registration will follow soon.
The abundance of text data and the advent of powerful deep learning models has led to rapid advancements in Natural Language Processing. However, adapting models to specialized tasks still requires a nuanced understanding of the data.
This adaption often requires human-annotated data that are considered to be time-consuming and labor-intensive. Active Learning addresses this by strategically involving humans in the learning loop, selecting instances for annotation that are most likely to maximize performance gains.
This approach not only optimizes human effort but also enhances the model’s adaptability to specific tasks with fewer annotated instances, making the training process more efficient and effective.
- Block 1: Introduction to Human-In-The-Loop Learning and (Deep) Active Learning
- Block 2: Hands-On / Practical session: Designing an (Deep) Active Learning Cycle
- Basic Python Skills
- Basic experience in Machine Learning/Deep Learning
- Bringing your own laptop to the workshop