Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring. - An Nguyen; Srijeet Chatterjee; Sven Weinzierl; Leo Schwinn; Martin Matzner; Bjoern M. Eskofier (ICPM Workshops, 2020)

Fast and Stable Adversarial Training through Noise Injection. - Leo Schwinn; Björn M. Eskofier (CoRR, 2020)

Conformance Checking for a Medical Training Process Using Petri net Simulation and Sequence Alignment. - An Nguyen; Wenyu Zhang; Leo Schwinn; Bjoern M. Eskofier (CoRR, 2020)

Sampled Nonlocal Gradients for Stronger Adversarial Attacks. - Leo Schwinn; Daniel Tenbrinck; An Nguyen; René Raab; Martin Burger 001; Bjoern M. Eskofier (CoRR, 2020)

Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks. - Leo Schwinn; An Nguyen; René Raab; Dario Zanca; Bjoern M. Eskofier; Daniel Tenbrinck; Martin Burger 001 (IJCNN, 2021)

CLIP: Cheap Lipschitz Training of Neural Networks. - Leon Bungert; René Raab; Tim Roith; Leo Schwinn; Daniel Tenbrinck (SSVM, 2021)

Identifying untrustworthy predictions in neural networks by geometric gradient analysis. - Leo Schwinn; An Nguyen; René Raab; Leon Bungert; Daniel Tenbrinck; Dario Zanca; Martin Burger 001; Björn M. Eskofier (UAI, 2021)

System Design for a Data-Driven and Explainable Customer Sentiment Monitor Using IoT and Enterprise Data. - An Nguyen; Stefan Foerstel; Thomas Kittler; Andrey Kurzyukov; Leo Schwinn; Dario Zanca; Tobias Hipp; Da Jun Sun; Michael Schrapp; Eva Rothgang; Björn M. Eskofier (IEEE Access, 2021)

System Design for a Data-driven and Explainable Customer Sentiment Monitor. - An Nguyen; Stefan Foerstel; Thomas Kittler; Andrey Kurzyukov; Leo Schwinn; Dario Zanca; Tobias Hipp; Da Jun Sun; Michael Schrapp; Eva Rothgang; Bjoern M. Eskofier (CoRR, 2021)

Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification. - Leo Schwinn; Leon Bungert; An Nguyen; René Raab; Falk Pulsmeyer; Doina Precup; Bjoern M. Eskofier; Dario Zanca (ICML, 2022)

Behind the Machine's Gaze: Biologically Constrained Neural Networks Exhibit Human-like Visual Attention. - Leo Schwinn; Doina Precup; Björn M. Eskofier; Dario Zanca (CoRR, 2022)

Simulating Human Gaze with Neural Visual Attention. - Leo Schwinn; Doina Precup; Björn M. Eskofier; Dario Zanca (CoRR, 2022)

xLength: Predicting Expected Ski Jump Length Shortly after Take-Off Using Deep Learning. - Johannes Link; Leo Schwinn; Falk Pulsmeyer; Thomas Kautz; Bjoern M. Eskofier (Sensors, 2022)

Behind the Machine's Gaze: Neural Networks with Biologically-inspired Constraints Exhibit Human-like Visual Attention. - Leo Schwinn; Doina Precup; Björn M. Eskofier; Dario Zanca (Trans. Mach. Learn. Res., 2022)

Scaled and Translated Image Recognition (STIR). - Thomas Altstidl; An Nguyen; Leo Schwinn; Franz Köferl; Christopher Mutschler; Björn M. Eskofier; Dario Zanca (Zenodo, 2022)

Scaled and Translated Image Recognition (STIR) Source Data. - Thomas Altstidl; An Nguyen; Leo Schwinn; Franz Köferl; Christopher Mutschler; Björn M. Eskofier; Dario Zanca (Zenodo, 2022)

FastAMI - a Monte Carlo Approach to the Adjustment for Chance in Clustering Comparison Metrics. - Kai Klede; Leo Schwinn; Dario Zanca; Björn M. Eskofier (AAAI, 2023)

Adversarial Attacks and Defenses in Large Language Models: Old and New Threats. - Leo Schwinn; David Dobre; Stephan Günnemann; Gauthier Gidel (ICBINB, 2023)

Just a Matter of Scale? Reevaluating Scale Equivariance in Convolutional Neural Networks. - Thomas Altstidl; An Nguyen; Leo Schwinn; Franz Köferl; Christopher Mutschler; Björn M. Eskofier; Dario Zanca (IJCNN, 2023)

Raising the Bar for Certified Adversarial Robustness with Diffusion Models. - Thomas Altstidl; David Dobre; Björn M. Eskofier; Gauthier Gidel; Leo Schwinn (CoRR, 2023)

Contrastive Language-Image Pretrained Models are Zero-Shot Human Scanpath Predictors. - Dario Zanca; Andrea Zugarini; Simon Dietz; Thomas R. Altstidl; Mark A. Turban Ndjeuha; Leo Schwinn; Bjoern M. Eskofier (CoRR, 2023)

Detektion, Quantifikation und Mitigation von Robustheitsanfälligkeiten in Tiefen Neuronalen Netzen. - Leo Schwinn (2023)

Exploring misclassifications of robust neural networks to enhance adversarial attacks. - Leo Schwinn; René Raab; An Nguyen; Dario Zanca; Björn M. Eskofier (Appl. Intell., 2023)

On the Scalability of Certified Adversarial Robustness with Generated Data. - Thomas Altstidl; David Dobre; Arthur Kosmala; Bjoern M. Eskofier; Gauthier Gidel; Leo Schwinn (NeurIPS, 2024)

Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space. - Leo Schwinn; David Dobre; Sophie Xhonneux; Gauthier Gidel; Stephan Günnemann (NeurIPS, 2024)

Efficient Adversarial Training in LLMs with Continuous Attacks. - Sophie Xhonneux; Alessandro Sordoni; Stephan Günnemann; Gauthier Gidel; Leo Schwinn (NeurIPS, 2024)

Efficient Time Series Processing for Transformers and State-Space Models through Token Merging. - Leon Götz; Marcel Kollovieh; Stephan Günnemann; Leo Schwinn (CoRR, 2024)

Large-Scale Dataset Pruning in Adversarial Training through Data Importance Extrapolation. - Björn Nieth; Thomas Altstidl; Leo Schwinn; Björn M. Eskofier (CoRR, 2024)

Relaxing Graph Transformers for Adversarial Attacks. - Philipp Foth; Lukas Gosch; Simon Geisler; Leo Schwinn; Stephan Günnemann (CoRR, 2024)

Revisiting the Robust Alignment of Circuit Breakers. - Leo Schwinn; Simon Geisler (CoRR, 2024)

Caption-Driven Explorations: Aligning Image and Text Embeddings through Human-Inspired Foveated Vision. - Dario Zanca; Andrea Zugarini; Simon Dietz; Thomas R. Altstidl; Mark A. Turban Ndjeuha; Leo Schwinn; Björn M. Eskofier (CoRR, 2024)

Extracting Unlearned Information from LLMs with Activation Steering. - Atakan Seyitoglu; Aleksei Kuvshinov; Leo Schwinn; Stephan Günnemann (CoRR, 2024)

Assessing Robustness via Score-Based Adversarial Image Generation. - Marcel Kollovieh; Lukas Gosch; Marten Lienen; Yan Scholten; Leo Schwinn; Stephan Günnemann (Trans. Mach. Learn. Res., 2024)

Artificial intelligence trend analysis on healthcare podcasts using topic modeling and sentiment analysis: a data-driven approach. - Philipp Dumbach; Leo Schwinn; Tim Löhr; Phi Long Do; Bjoern M. Eskofier (Evol. Intell., 2024)

Generalized Synchronized Active Learning for Multi-Agent-Based Data Selection on Mobile Robotic Systems. - Sebastian Schmidt 006; Lukas Stappen; Leo Schwinn; Stephan Günnemann (IEEE Robotics Autom. Lett., 2024)

Joint Out-of-Distribution Filtering and Data Discovery Active Learning. - Sebastian Schmidt 006; Leonard Schenk; Leo Schwinn; Stephan Günnemann (CVPR, 2025)

Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting. - Marcel Kollovieh; Marten Lienen; David Lüdke; Leo Schwinn; Stephan Günnemann (ICLR, 2025)

A Probabilistic Perspective on Unlearning and Alignment for Large Language Models. - Yan Scholten; Stephan Günnemann; Leo Schwinn (ICLR, 2025)

A Unified Approach Towards Active Learning and Out-of-Distribution Detection. - Sebastian Schmidt 006; Leonard Schenk; Leo Schwinn; Stephan Günnemann (Trans. Mach. Learn. Res., 2025)

Fast Proxies for LLM Robustness Evaluation. - Tim Beyer; Jan Schuchardt; Leo Schwinn; Stephan Günnemann (CoRR, 2025)

Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives. - Leo Schwinn; Yan Scholten; Tom Wollschläger; Sophie Xhonneux; Stephen Casper; Stephan Günnemann; Gauthier Gidel (CoRR, 2025)

A generative approach to LLM harmfulness detection with special red flag tokens. - Sophie Xhonneux; David Dobre; Mehrnaz Mohfakhami; Leo Schwinn; Gauthier Gidel (CoRR, 2025)

LLM-Safety Evaluations Lack Robustness. - Tim Beyer; Sophie Xhonneux; Simon Geisler; Gauthier Gidel; Leo Schwinn; Stephan Günnemann (CoRR, 2025)

Understanding Cross-Model Perceptual Invariances Through Ensemble Metamers. - Lukas Boehm; Jonas Leo Mueller; Christoffer Löffler; Leo Schwinn; Björn M. Eskofier; Dario Zanca (CoRR, 2025)

Byte Pair Encoding for Efficient Time Series Forecasting. - Leon Götz; Marcel Kollovieh; Stephan Günnemann; Leo Schwinn (CoRR, 2025)

Joint Relational Database Generation via Graph-Conditional Diffusion Models. - Mohamed Amine Ketata; David Lüdke; Leo Schwinn; Stephan Günnemann (CoRR, 2025)

Effective Data Pruning through Score Extrapolation. - Sebastian Schmidt 006; Prasanga Dhungel; Christoffer Löffler; Björn Nieth; Stephan Günnemann; Leo Schwinn (CoRR, 2025)

FOCUS: Internal MLLM Representations for Efficient Fine-Grained Visual Question Answering. - Liangyu Zhong; Fabio Rosenthal; Joachim Sicking; Fabian Hüger; Thorsten Bagdonat; Hanno Gottschalk; Leo Schwinn (CoRR, 2025)

Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs. - Yan Scholten; Sophie Xhonneux; Leo Schwinn; Stephan Günnemann (CoRR, 2025)

Tail-aware Adversarial Attacks: A Distributional Approach to Efficient LLM Jailbreaking. - Tim Beyer; Yan Scholten; Leo Schwinn; Stephan Günnemann (CoRR, 2025)

Investigating the Robustness of Retrieval-Augmented Generation at the Query Level. - Sezen Perçin; Xin Su 008; Syed Sha Qutub; Phillip Howard; Aleksei Kuvshinov; Leo Schwinn; Kay-Ulrich Scholl (CoRR, 2025)

Artificial intelligence trend analysis in German business and politics: a web mining approach. - Philipp Dumbach; Leo Schwinn; Tim Löhr; Tassilo Elsberger; Bjoern M. Eskofier (Int. J. Data Sci. Anal., 2025)