3 papers
stat.AP2026
Country-wide, high-resolution monitoring of forest browning with Sentinel-2
Samantha Biegel, David Brüggemann, Francesco Grossi +3
Natural and anthropogenic disturbances are impacting the health of forests worldwide. Monitoring forest disturbances at scale is important to inform conservation efforts. Here, we…
cs.LG2023
Improving Retrieval-Augmented Large Language Models via Data Importance Learning
Xiaozhong Lyu, Stefan Grafberger, Samantha Biegel +4
Retrieval augmentation enables large language models to take advantage of external knowledge, for example on tasks like question answering and data imputation. However, the perform…
cs.LG2021
Active WeaSuL: Improving Weak Supervision with Active Learning
Samantha Biegel, Rafah El-Khatib, Luiz Otavio Vilas Boas Oliveira +2
The availability of labelled data is one of the main limitations in machine learning. We can alleviate this using weak supervision: a framework that uses expert-defined rules $\bol…