1 citations · 1 across the 8 of their papers we have counts for
8 papers
The Role of Active Learning in Modern Machine Learning
Thorben Werner, Lars Schmidt-Thieme, Vijaya Krishna Yalavarthi
Even though Active Learning (AL) is widely studied, it is rarely applied in contexts outside its own scientific literature. We posit that the reason for this is AL's high computati…
Bayesian Active Learning By Distribution Disagreement
Thorben Werner, Lars Schmidt-Thieme
Active Learning (AL) for regression has been systematically under-researched due to the increased difficulty of measuring uncertainty in regression models. Since normalizing flows…
A Cross-Domain Benchmark for Active Learning
Thorben Werner, Johannes Burchert, Maximilian Stubbemann +1
Active Learning (AL) deals with identifying the most informative samples for labeling to reduce data annotation costs for supervised learning tasks. AL research suffers from the fa…
Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training
Johannes Burchert, Thorben Werner, Vijaya Krishna Yalavarthi +3
As with most other data domains, EEG data analysis relies on rich domain-specific preprocessing. Beyond such preprocessing, machine learners would hope to deal with such data as wi…
Towards Comparable Active Learning
Thorben Werner, Johannes Burchert, Lars Schmidt-Thieme
Active Learning has received significant attention in the field of machine learning for its potential in selecting the most informative samples for labeling, thereby reducing data…
Towards Comparable Knowledge Distillation in Semantic Image Segmentation
Onno Niemann, Christopher Vox, Thorben Werner
Knowledge Distillation (KD) is one proposed solution to large model sizes and slow inference speed in semantic segmentation. In our research we identify 25 proposed distillation lo…
When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development
Nghia Duong-Trung, Stefan Born, Jong Woo Kim +9
Machine learning (ML) is becoming increasingly crucial in many fields of engineering but has not yet played out its full potential in bioprocess engineering. While experimentation…
Reinforcement Learning Approach to Active Learning for Image Classification
Thorben Werner
Machine Learning requires large amounts of labeled data to fit a model. Many datasets are already publicly available, nevertheless forcing application possibilities of machine lear…