Daniel Kottke

Publication Activity (10 Years)

Years Active: 2014-2023
Publications (10 Years): 25

Top Topics

Stopping Criterion

Uncertainty Sampling

Semantic Segmentation

Top Venues

Publications

Tuan Pham Minh, Jayan Wijesingha, Daniel Kottke, Marek Herde, Denis Huseljic, Bernhard Sick, Michael Wachendorf, Thomas Esch
Active Label Refinement for Semantic Segmentation of Satellite Images. CoRR (2023)
Christoph Sandrock, Marek Herde, Daniel Kottke, Bernhard Sick
Exploring the Potential of Optimal Active Learning via a Non-myopic Oracle Policy. DS (2023)
Daniel Kottke, Christoph Sandrock, Georg Krempl, Bernhard Sick
A Stopping Criterion for Transductive Active Learning. ECML/PKDD (4) (2022)
Marek Herde, Zhixin Huang, Denis Huseljic, Daniel Kottke, Stephan Vogt, Bernhard Sick
Fast Bayesian Updates for Deep Learning with a Use Case in Active Learning. CoRR (2022)
Adrian Englhardt, Holger Trittenbach, Daniel Kottke, Bernhard Sick, Klemens Böhm
Efficient SVDD sampling with approximation guarantees for the decision boundary. Mach. Learn. 111 (4) (2022)
Tuan Pham, Daniel Kottke, Georg Krempl, Bernhard Sick
Stream-based active learning for sliding windows under the influence of verification latency. Mach. Learn. 111 (6) (2022)
Georg Krempl, Daniel Kottke, Tuan Pham
Statistical Analysis of Pairwise Connectivity. DS (2021)
Daniel Kottke, Marek Herde, Christoph Sandrock, Denis Huseljic, Georg Krempl, Bernhard Sick
Toward optimal probabilistic active learning using a Bayesian approach. Mach. Learn. 110 (6) (2021)
Georg Krempl, Daniel Kottke, Tuan Pham Minh
ACE: A Novel Approach for the Statistical Analysis of Pairwise Connectivity. CoRR (2021)
Daniel Kottke, Georg Krempl, Marianne Stecklina, Cornelius Styp von Rekowski, Tim Sabsch, Tuan Pham Minh, Matthias Deliano, Myra Spiliopoulou, Bernhard Sick
Probabilistic Active Learning for Active Class Selection. CoRR (2021)
Daniel Kottke, Marek Herde, Christoph Sandrock, Denis Huseljic, Georg Krempl, Bernhard Sick
Toward Optimal Probabilistic Active Learning Using a Bayesian Approach. CoRR (2020)
Adrian Englhardt, Holger Trittenbach, Daniel Kottke, Bernhard Sick, Klemens Böhm
Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary. CoRR (2020)
Marek Herde, Daniel Kottke, Denis Huseljic, Bernhard Sick
Multi-Annotator Probabilistic Active Learning. ICPR (2020)
Tuan Pham Minh, Daniel Kottke, Anna Tsarenko, Christian Gruhl, Bernhard Sick
Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning. IJCNN (2020)
Denis Huseljic, Bernhard Sick, Marek Herde, Daniel Kottke
Separation of Aleatoric and Epistemic Uncertainty in Deterministic Deep Neural Networks. ICPR (2020)
Daniel Kottke, Jim Schellinger, Denis Huseljic, Bernhard Sick
Limitations of Assessing Active Learning Performance at Runtime. CoRR (2019)
Christoph Sandrock, Marek Herde, Adrian Calma, Daniel Kottke, Bernhard Sick
Combining Self-reported Confidences from Uncertain Annotators to Improve Label Quality. IJCNN (2019)
Daniel Kottke, Adrian Calma, Denis Huseljic, Christoph Sandrock, George Kachergis, Bernhard Sick
The Other Human in The Loop - A Pilot Study to Find Selection Strategies for Active Learning. IJCNN (2018)
Marek Herde, Daniel Kottke, Adrian Calma, Maarten Bieshaar, Stephan Deist, Bernhard Sick
Active Sorting - An Efficient Training of a Sorting Robot with Active Learning Techniques. IJCNN (2018)
Adrian Calma, Moritz Stolz, Daniel Kottke, Sven Tomforde, Bernhard Sick
Active Learning With Realistic Data - A Case Study. IJCNN (2018)
Dominik Lang, Daniel Kottke, Georg Krempl
Probabilistic Active Learning with Structure-Sensitive Kernels. IAL@PKDD/ECML (2017)
Daniel Kottke, Adrian Calma, Denis Huseljic, Georg Krempl, Bernhard Sick
Challenges of Reliable, Realistic and Comparable Active Learning Evaluation. IAL@PKDD/ECML (2017)
Adrian Calma, Daniel Kottke, Bernhard Sick, Sven Tomforde
Learning to Learn: Dynamic Runtime Exploitation of Various Knowledge Sources and Machine Learning Paradigms. FAS*W@SASO/ICCAC (2017)
Dominik Lang, Daniel Kottke, Georg Krempl, Myra Spiliopoulou
Investigating Exploratory Capabilities of Uncertainty Sampling using SVMs in Active Learning. AL@iKNOW (2016)
Daniel Kottke, Georg Krempl, Dominik Lang, Johannes Teschner, Myra Spiliopoulou
Multi-Class Probabilistic Active Learning. ECAI (2016)
Georg Krempl, Daniel Kottke, Vincent Lemaire
Optimised probabilistic active learning (OPAL) - For fast, non-myopic, cost-sensitive active classification. Mach. Learn. 100 (2-3) (2015)
Daniel Kottke, Gino Gulamhussene, Klaus D. Tönnies
Data-Driven Spine Detection for Multi-Sequence MRI. Bildverarbeitung für die Medizin (2015)
Daniel Kottke, Georg Krempl, Myra Spiliopoulou
Probabilistic Active Learning in Datastreams. IDA (2015)
Georg Krempl, Daniel Kottke, Myra Spiliopoulou
Probabilistic Active Learning: A Short Proposition. ECAI (2014)
Georg Krempl, Daniel Kottke, Myra Spiliopoulou
Probabilistic Active Learning: Towards Combining Versatility, Optimality and Efficiency. Discovery Science (2014)