31 citations · 50 across the 5 of their papers we have counts for
5 papers
Pruning Extensions and Efficiency Trade-Offs for Sustainable Time Series Classification
Raphael Fischer, Angus Dempster, Sebastian Buschjäger +3
Time series classification (TSC) enables important use cases, however lacks a unified understanding of performance trade-offs across models, datasets, and hardware. While resource…
Energy Efficiency Considerations for Popular AI Benchmarks
Raphael Fischer, Matthias Jakobs, Katharina Morik
Advances in artificial intelligence need to become more resource-aware and sustainable. This requires clear assessment and reporting of energy efficiency trade-offs, like sacrifici…
The Care Label Concept: A Certification Suite for Trustworthy and Resource-Aware Machine Learning
Katharina Morik, Helena Kotthaus, Lukas Heppe +4
Machine learning applications have become ubiquitous. This has led to an increased effort of making machine learning trustworthy. Explainable and fair AI have already matured. They…
Explainable Machine Learning with Prior Knowledge: An Overview
Katharina Beckh, Sebastian Müller, Matthias Jakobs +6
This survey presents an overview of integrating prior knowledge into machine learning systems in order to improve explainability. The complexity of machine learning models has elic…
Yes We Care! -- Certification for Machine Learning Methods through the Care Label Framework
Katharina Morik, Helena Kotthaus, Raphael Fischer +6
Machine learning applications have become ubiquitous. Their applications range from embedded control in production machines over process optimization in diverse areas (e.g., traffi…