Roland S. Zimmermann

Affiliations:
  • University of Tübingen, Germany
  • Volkswagen AG Wolfsburg, Germany (former)
  • University of Göttingen, Germany (former)


According to our database1, Roland S. Zimmermann authored at least 20 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

2019
2020
2021
2022
2023
2024
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Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
In Search of Forgotten Domain Generalization.
CoRR, 2024

InfoNCE: Identifying the Gap Between Theory and Practice.
CoRR, 2024

Don't trust your eyes: on the (un)reliability of feature visualizations.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Sensitivity of Slot-Based Object-Centric Models to their Number of Slots.
CoRR, 2023

Scale Alone Does not Improve Mechanistic Interpretability in Vision Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Provably Learning Object-Centric Representations.
Proceedings of the International Conference on Machine Learning, 2023

2022
Increasing Confidence in Adversarial Robustness Evaluations.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
Score-Based Generative Classifiers.
CoRR, 2021

How Well do Feature Visualizations Support Causal Understanding of CNN Activations?
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Contrastive Learning Inverts the Data Generating Process.
Proceedings of the 38th International Conference on Machine Learning, 2021

Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
Foolbox Native: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX.
J. Open Source Softw., 2020

Reconstructing Complex Cardiac Excitation Waves From Incomplete Data Using Echo State Networks and Convolutional Autoencoders.
Frontiers Appl. Math. Stat., 2020

Exemplary Natural Images Explain CNN Activations Better than Feature Visualizations.
CoRR, 2020

Increasing the robustness of DNNs against image corruptions by playing the Game of Noise.
CoRR, 2020

A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions.
Proceedings of the Computer Vision - ECCV 2020, 2020

A Self-Supervised Feature Map Augmentation (FMA) Loss and Combined Augmentations Finetuning to Efficiently Improve the Robustness of CNNs.
Proceedings of the CSCS '20: Computer Science in Cars Symposium, 2020

2019
Faster training of Mask R-CNN by focusing on instance boundaries.
Comput. Vis. Image Underst., 2019

Comment on "Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network".
CoRR, 2019

Simion Zoo: A Workbench for Distributed Experimentation with Reinforcement Learning for Continuous Control Tasks.
CoRR, 2019


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