Maximilian Dreyer

According to our database, Maximilian Dreyer authored at least 13 papers between 2020 and 2024.

Collaborative distances:

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

2024

Pruning By Explaining Revisited: Optimizing Attribution Methods to Prune CNNs and Transformers.

Sayed Mohammad Vakilzadeh Hatefi, Maximilian Dreyer, Reduan Achtibat, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin

CoRR, 2024

PURE: Turning Polysemantic Neurons Into Pure Features by Identifying Relevant Circuits.

Maximilian Dreyer, Erblina Purelku, Johanna Vielhaben, Wojciech Samek, Sebastian Lapuschkin

CoRR, 2024

Explainable Concept Mappings of MRI: Revealing the Mechanisms Underlying Deep Learning-Based Brain Disease Classification.

Christian Tinauer, Anna Damulina, Maximilian Sackl, Martin Soellradl, Reduan Achtibat, Maximilian Dreyer, Frederik Pahde, Sebastian Lapuschkin, Reinhold Schmidt, Stefan Ropele, Wojciech Samek, Christian Langkammer

Proceedings of the Explainable Artificial Intelligence, 2024

AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformers.

Reduan Achtibat, Sayed Mohammad Vakilzadeh Hatefi, Maximilian Dreyer, Aakriti Jain, Thomas Wiegand, Sebastian Lapuschkin, Wojciech Samek

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Understanding the (Extra-)Ordinary: Validating Deep Model Decisions with Prototypical Concept-based Explanations.

Maximilian Dreyer, Reduan Achtibat, Wojciech Samek, Sebastian Lapuschkin

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression.

Dilyara Bareeva, Maximilian Dreyer, Frederik Pahde, Wojciech Samek, Sebastian Lapuschkin

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space.

Maximilian Dreyer, Frederik Pahde, Christopher J. Anders, Wojciech Samek, Sebastian Lapuschkin

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

From attribution maps to human-understandable explanations through Concept Relevance Propagation.

Reduan Achtibat, Maximilian Dreyer, Ilona Eisenbraun, Sebastian Bosse, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin

Nat. Mac. Intell., September, 2023

From Hope to Safety: Unlearning Biases of Deep Models by Enforcing the Right Reasons in Latent Space.

Maximilian Dreyer, Frederik Pahde, Christopher J. Anders, Wojciech Samek, Sebastian Lapuschkin

CoRR, 2023

Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models.

Frederik Pahde, Maximilian Dreyer, Wojciech Samek, Sebastian Lapuschkin

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations.

Maximilian Dreyer, Reduan Achtibat, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

From "Where" to "What": Towards Human-Understandable Explanations through Concept Relevance Propagation.

Reduan Achtibat, Maximilian Dreyer, Ilona Eisenbraun, Sebastian Bosse, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin

CoRR, 2022

2020

ECQ^x: Explainability-Driven Quantization for Low-Bit and Sparse DNNs.

Daniel Becking, Maximilian Dreyer, Wojciech Samek, Karsten Müller, Sebastian Lapuschkin

Proceedings of the xxAI - Beyond Explainable AI, 2020