Sick, Bernhard

25 publications

ICLR 2025 BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics Lukas Rauch, Raphael Schwinger, Moritz Wirth, René Heinrich, Denis Huseljic, Marek Herde, Jonas Lange, Stefan Kahl, Bernhard Sick, Sven Tomforde, Christoph Scholz

TMLR 2025 Can Masked Autoencoders Also Listen to Birds? Lukas Rauch, René Heinrich, Ilyass Moummad, Alexis Joly, Bernhard Sick, Christoph Scholz

TMLR 2025 Crowd-Hpo: Realistic Hyperparameter Optimization and Benchmarking for Learning from Crowds with Noisy Labels Marek Herde, Lukas Lührs, Denis Huseljic, Bernhard Sick

ECML-PKDD 2025 Efficient Bayesian Updates for Deep Active Learning via Laplace Approximations Denis Huseljic, Marek Herde, Lukas Rauch, Paul Hahn, Zhixin Huang, Daniel Kottke, Stephan Vogt, Bernhard Sick

TMLR 2025 Flow-Attentional Graph Neural Networks Pascal Plettenberg, Dominik Köhler, Bernhard Sick, Josephine Thomas

ECML-PKDD 2025 Graph Neural Networks for Automatic Addition of Optimizing Components in Printed Circuit Board Schematics Pascal Plettenberg, André Alcalde, Bernhard Sick, Josephine M. Thomas

ECML-PKDD 2025 Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approach Mohamed Hassouna, Clara Holzhüter, Malte Lehna, Matthijs de Jong, Jan Viebahn, Bernhard Sick, Christoph Scholz

NeurIPS 2024 Dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple Humans Marek Herde, Denis Huseljic, Lukas Rauch, Bernhard Sick

ECML-PKDD 2024 Enhancing Multi-Objective Optimisation Through Machine Learning-Supported Multiphysics Simulation Diego Botache, Jens Decke, Winfried Ripken, Abhinay Dornipati, Franz Götz-Hahn, Mohamed Ayeb, Bernhard Sick

ECML-PKDD 2024 Fast Fishing: Approximating Bait for Efficient and Scalable Deep Active Image Classification Denis Huseljic, Paul Hahn, Marek Herde, Lukas Rauch, Bernhard Sick

ECCVW 2024 Reliable Probabilistic Human Trajectory Prediction for Autonomous Applications Manuel Hetzel, Hannes Reichert, Konrad Doll, Bernhard Sick

CVPRW 2024 Sensor Equivariance: A Framework for Semantic Segmentation with Diverse Camera Models Hannes Reichert, Manuel Hetzel, Andreas Hubert, Konrad Doll, Bernhard Sick

TMLR 2024 The Interplay of Uncertainty Modeling and Deep Active Learning: An Empirical Analysis in Image Classification Denis Huseljic, Marek Herde, Yannick Nagel, Lukas Rauch, Paulius Strimaitis, Bernhard Sick

ECML-PKDD 2023 ActiveGLAE: A Benchmark for Deep Active Learning with Transformers Lukas Rauch, Matthias Aßenmacher, Denis Huseljic, Moritz Wirth, Bernd Bischl, Bernhard Sick

TMLR 2023 Multi-Annotator Deep Learning: A Probabilistic Framework for Classification Marek Herde, Denis Huseljic, Bernhard Sick

ICCVW 2023 What Does Really Count? Estimating Relevance of Corner Cases for Semantic Segmentation in Automated Driving Jasmin Breitenstein, Florian Heidecker, Maria Lyssenko, Daniel Bogdoll, Maarten Bieshaar, J. Marius Zöllner, Bernhard Sick, Tim Fingscheidt

ECML-PKDD 2022 A Stopping Criterion for Transductive Active Learning Daniel Kottke, Christoph Sandrock, Georg Krempl, Bernhard Sick

MLJ 2022 Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary Adrian Englhardt, Holger Trittenbach, Daniel Kottke, Bernhard Sick, Klemens Böhm

MLJ 2022 Stream-Based Active Learning for Sliding Windows Under the Influence of Verification Latency Tuan Pham, Daniel Kottke, Georg Krempl, Bernhard Sick

ICCVW 2021 About the Ambiguity of Data Augmentation for 3D Object Detection in Autonomous Driving Matthias Reuse, Martin Simon, Bernhard Sick

ICCVW 2021 Description of Corner Cases in Automated Driving: Goals and Challenges Daniel Bogdoll, Jasmin Breitenstein, Florian Heidecker, Maarten Bieshaar, Bernhard Sick, Tim Fingscheidt, J. Marius Zöllner

CVPRW 2021 Out-of-Distribution Detection and Generation Using Soft Brownian Offset Sampling and Autoencoders Felix Möller, Diego Botache, Denis Huseljic, Florian Heidecker, Maarten Bieshaar, Bernhard Sick

ECML-PKDD 2021 Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time Series Forecast Jens Schreiber, Stephan Vogt, Bernhard Sick

MLJ 2021 Toward Optimal Probabilistic Active Learning Using a Bayesian Approach Daniel Kottke, Marek Herde, Christoph Sandrock, Denis Huseljic, Georg Krempl, Bernhard Sick

UAI 2009 Lower Bound Bayesian Networks - An Efficient Inference of Lower Bounds on Probability Distributions in Bayesian Networks Daniel Andrade, Bernhard Sick