307 citations · 465 across the 30 of their papers we have counts for
8 papers · 2 filters
Shrub Ensembles for Online Classification
Sebastian Buschjäger, Sibylle Hess, Katharina Morik
Online learning algorithms have become a ubiquitous tool in the machine learning toolbox and are frequently used in small, resource-constraint environments. Among the most successf…
There is no Double-Descent in Random Forests
Sebastian Buschjäger, Katharina Morik
Random Forests (RFs) are among the state-of-the-art in machine learning and offer excellent performance with nearly zero parameter tuning. Remarkably, RFs seem to be impervious to…
Improving the Accuracy-Memory Trade-Off of Random Forests Via Leaf-Refinement
Sebastian Buschjäger, Katharina Morik
Random Forests (RF) are among the state-of-the-art in many machine learning applications. With the ongoing integration of ML models into everyday life, the deployment and continuou…
Explaining Deep Learning Representations by Tracing the Training Process
Lukas Pfahler, Katharina Morik
We propose a novel explanation method that explains the decisions of a deep neural network by investigating how the intermediate representations at each layer of the deep network w…
Noisy Labels for Weakly Supervised Gamma Hadron Classification
Lukas Pfahler, Mirko Bunse, Katharina Morik
Gamma hadron classification, a central machine learning task in gamma ray astronomy, is conventionally tackled with supervised learning. However, the supervised approach requires a…
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…
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…
Bit Error Tolerance Metrics for Binarized Neural Networks
Sebastian Buschjäger, Jian-Jia Chen, Kuan-Hsun Chen +5
To reduce the resource demand of neural network (NN) inference systems, it has been proposed to use approximate memory, in which the supply voltage and the timing parameters are tu…