Leo Schwinn

According to our database, Leo Schwinn authored at least 22 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

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

CoRR, 2024

2023

Exploring misclassifications of robust neural networks to enhance adversarial attacks.

Leo Schwinn, René Raab, An Nguyen, Dario Zanca, Björn M. Eskofier

Appl. Intell., September, 2023

Detektion, Quantifikation und Mitigation von Robustheitsanfälligkeiten in Tiefen Neuronalen Netzen.

Leo Schwinn

PhD thesis, 2023

Adversarial Attacks and Defenses in Large Language Models: Old and New Threats.

Leo Schwinn, David Dobre, Stephan Günnemann, Gauthier Gidel

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

Raising the Bar for Certified Adversarial Robustness with Diffusion Models.

Thomas Altstidl, David Dobre, Björn M. Eskofier, Gauthier Gidel, Leo Schwinn

CoRR, 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

Proceedings of the International Joint Conference on Neural Networks, 2023

FastAMI - a Monte Carlo Approach to the Adjustment for Chance in Clustering Comparison Metrics.

Kai Klede, Leo Schwinn, Dario Zanca, Björn M. Eskofier

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

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

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

Simulating Human Gaze with Neural Visual Attention.

Leo Schwinn, Doina Precup, Björn M. Eskofier, Dario Zanca

CoRR, 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

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

Proceedings of the International Conference on Machine Learning, 2022

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

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

Identifying untrustworthy predictions in neural networks by geometric gradient analysis.

Leo Schwinn, An Nguyen, René Raab, Leon Bungert, Daniel Tenbrinck, Dario Zanca, Martin Burger, Björn M. Eskofier

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

CLIP: Cheap Lipschitz Training of Neural Networks.

Leon Bungert, René Raab, Tim Roith, Leo Schwinn, Daniel Tenbrinck

Proceedings of the Scale Space and Variational Methods in Computer Vision, 2021

Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks.

Leo Schwinn, An Nguyen, René Raab, Dario Zanca, Bjoern M. Eskofier, Daniel Tenbrinck, Martin Burger

Proceedings of the International Joint Conference on Neural Networks, 2021

2020

Sampled Nonlocal Gradients for Stronger Adversarial Attacks.

Leo Schwinn, Daniel Tenbrinck, An Nguyen, René Raab, Martin Burger, Bjoern 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

Fast and Stable Adversarial Training through Noise Injection.

Leo Schwinn, Björn M. Eskofier

CoRR, 2020

Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring.

An Nguyen, Srijeet Chatterjee, Sven Weinzierl, Leo Schwinn, Martin Matzner, Bjoern M. Eskofier

Proceedings of the Process Mining Workshops, 2020