Stream-based active learning (AL) strategies minimize the labeling effort by querying labels that improve the classifier's performance the most. So far, these strategies neglect the fact that an oracle or expert requires time to provide a queried label. We show that existing AL methods deteriorate or even fail under the influence of such verification latency. The problem with these methods is that they estimate a label's utility on the currently available labeled data. However, when this label would arrive, some of the current data may have gotten outdated and new labels have arrived. In this article, we propose to simulate the available data at the time when the label would arrive. Therefore, our method Forgetting and Simulating (FS) forgets outdated information and simulates the delayed labels to get more realistic utility estimates. We assume to know the label's arrival date a priori and the classifier's training data to be bounded by a sliding window. Our extensive experiments show that FS improves stream-based AL strategies in settings with both, constant and variable verification latency.

@article{pham2022stream, author = {Pham, Tuan and Kottke, Daniel and Krempl, Georg and Sick, Bernhard}, title = {Stream-based active learning for sliding windows under the influence of verification latency}, journal = {Machine Learning}, publisher = {Springer}, year = {2022}, volume = {111}, number = {6}, pages = {2011--2036}, doi = {doi.org/10.1007/s10994-021-06099-z}, keywords = {imported, itegpub, isac-www}, abstract = {Stream-based active learning (AL) strategies minimize the labeling effort by querying labels that improve the classifier's performance the most. So far, these strategies neglect the fact that an oracle or expert requires time to provide a queried label. We show that existing AL methods deteriorate or even fail under the influence of such verification latency. The problem with these methods is that they estimate a label's utility on the currently available labeled data. However, when this label would arrive, some of the current data may have gotten outdated and new labels have arrived. In this article, we propose to simulate the available data at the time when the label would arrive. Therefore, our method Forgetting and Simulating (FS) forgets outdated information and simulates the delayed labels to get more realistic utility estimates. We assume to know the label's arrival date a priori and the classifier's training data to be bounded by a sliding window. Our extensive experiments show that FS improves stream-based AL strategies in settings with both, constant and variable verification latency. } }

%0 = article %A = Pham, Tuan and Kottke, Daniel and Krempl, Georg and Sick, Bernhard %D = 2022 %I = Springer %T = Stream-based active learning for sliding windows under the influence of verification latency

@book{tomforde2024organic,, title = {Organic Computing -- Doctoral Dissertation Colloquium 2023}, editor = {Tomforde, Sven and Krupitzer, Christian}, series = {Intelligent Embedded Systems}, publisher = {kassel university press}, year = {2024}, volume = {26}, doi = {10.17170/kobra-202402269661}, keywords = {imported, itegpub, isac-www} }

%0 = book %D = 2024 %I = kassel university press %T = Organic Computing -- Doctoral Dissertation Colloquium 2023

@book{tomforde2023organic,, title = {Organic Computing -- Doctoral Dissertation Colloquium 2022}, editor = {Tomforde, Sven and Krupitzer, Christian}, series = {Intelligent Embedded Systems}, publisher = {kassel university press}, year = {2023}, volume = {24}, doi = {10.17170/kobra-202302107484}, keywords = {imported, itegpub, isac-www} }

%0 = book %D = 2023 %I = kassel university press %T = Organic Computing -- Doctoral Dissertation Colloquium 2022

This article investigates the application of computer vision and graph-based models in solving mesh-based partial differential equations within high-performance computing environments. Focusing on structured, graded structured, and unstructured meshes, the study compares the performance and computational efficiency of three computer vision-based models against three graph-based models across three datasets. The research aims to identify the most suitable models for different mesh topographies, particularly highlighting the exploration of graded meshes, a less studied area. Results demonstrate that computer vision-based models, notably U-Net, outperform the graph models in prediction performance and efficiency in two (structured and graded) out of three mesh topographies. The study also reveals the unexpected effectiveness of computer vision-based models in handling unstructured meshes, suggesting a potential shift in methodological approaches for data-driven partial differential equation learning. The article underscores deep learning as a viable and potentially sustainable way to enhance traditional high-performance computing methods, advocating for informed model selection based on the topography of the mesh.

@inproceedings{decke2024structured, author = {Decke, Jens and Wünsch, Olaf and Sick, Bernhard and Gruhl, Christian}, title = {From Structured to Unstructured: A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs}, booktitle = {International Conference on Architecture of Computing Systems (ARCS)}, publisher = {Springer}, year = {2024}, note = {(accepted)}, keywords = {imported, itegpub, isac-www, Organic, Computing, Self-Optimization, DeepLearning, PartialDifferentialEquation, SurrogateModel, MeshTopographies}, abstract = {This article investigates the application of computer vision and graph-based models in solving mesh-based partial differential equations within high-performance computing environments. Focusing on structured, graded structured, and unstructured meshes, the study compares the performance and computational efficiency of three computer vision-based models against three graph-based models across three datasets. The research aims to identify the most suitable models for different mesh topographies, particularly highlighting the exploration of graded meshes, a less studied area. Results demonstrate that computer vision-based models, notably U-Net, outperform the graph models in prediction performance and efficiency in two (structured and graded) out of three mesh topographies. The study also reveals the unexpected effectiveness of computer vision-based models in handling unstructured meshes, suggesting a potential shift in methodological approaches for data-driven partial differential equation learning. The article underscores deep learning as a viable and potentially sustainable way to enhance traditional high-performance computing methods, advocating for informed model selection based on the topography of the mesh.} }

%0 = inproceedings %A = Decke, Jens and Wünsch, Olaf and Sick, Bernhard and Gruhl, Christian %B = International Conference on Architecture of Computing Systems (ARCS) %D = 2024 %I = Springer %T = From Structured to Unstructured: A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs

This article presents the Sorting Composite Quantile Regression Neural Network (SCQRNN), an advanced quantile regression model designed to prevent quantile crossing and enhance computational efficiency. Integrating ad hoc sorting in training, the SCQRNN ensures non-intersecting quantiles, boosting model reliability and interpretability. We demonstrate that the SCQRNN not only prevents quantile crossing and reduces computational complexity but also achieves faster convergence than traditional models. This advancement meets the requirements of high-performance computing for sustainable, accurate computation. In organic computing, the SCQRNN enhances self-aware systems with predictive uncertainties, enriching applications across finance, meteorology, climate science, and engineering.

@inproceedings{decke2024efficient, author = {Decke, Jens and Jenß, Arne and Sick, Bernhard and Gruhl, Christian}, title = {An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing}, booktitle = {International Conference on Architecture of Computing Systems (ARCS)}, publisher = {Springer}, year = {2024}, note = {(accepted)}, keywords = {imported, itegpub, isac-www, QuantileRegression, QuantileCrossing, OrganicComputing, Self-Awareness, DifferentiableSorting}, abstract = {This article presents the Sorting Composite Quantile Regression Neural Network (SCQRNN), an advanced quantile regression model designed to prevent quantile crossing and enhance computational efficiency. Integrating ad hoc sorting in training, the SCQRNN ensures non-intersecting quantiles, boosting model reliability and interpretability. We demonstrate that the SCQRNN not only prevents quantile crossing and reduces computational complexity but also achieves faster convergence than traditional models. This advancement meets the requirements of high-performance computing for sustainable, accurate computation. In organic computing, the SCQRNN enhances self-aware systems with predictive uncertainties, enriching applications across finance, meteorology, climate science, and engineering.} }

%0 = inproceedings %A = Decke, Jens and Jenß, Arne and Sick, Bernhard and Gruhl, Christian %B = International Conference on Architecture of Computing Systems (ARCS) %D = 2024 %I = Springer %T = An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing