Moritz Böhle

I am a post doc researcher at Kyutai in Paris. Before that, I did a PhD at the Computer Vision and Machine Learning group at the Max Planck Institute for Informatics in Saarbrücken, Germany, where I was advised by Prof. Bernt Schiele and Prof. Mario Fritz. My research is focused on interpretable, trustworthy, and responsible deep learning. Before commencing my doctoral studies, I obtained a Bachelor’s degree in Physics at the Free University in Berlin, Germany, in 2016 and a Master’s degree at the Bernstein Center for Computational Neuroscience in Berlin, Germany, in 2019.

Email / CV / Google Scholar / Github / LinkedIn

Publications

	B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable Shreyash Arya, Sukrut Rao, Moritz Böhle, Bernt Schiele (equal contribution) NeurIPS, 2024 Paper \| Code
	Discover-then-name: Task-agnostic Concept Bottlenecks via Automated Concept Discovery Sweta Mahajan, Sukrut Rao, Moritz Böhle, Bernt Schiele (equal contribution) ECCV, 2024* Paper \| Code \| Video \| Slides
	Good Teachers Explain: Explanation-Enhanced Knowledge Distillation Amin Parchami-Araghi, Moritz Böhle, Sukrut Rao, Bernt Schiele (equal contribution) ECCV, 2024 Paper \| Code
	B-cos Alignment for Inherently Interpretable CNNs and Vision Transformers Moritz Böhle, Navdeeppal Singh, Mario Fritz, Bernt Schiele TPAMI, 2024 Paper \| Code \| Talk @ MIT Vision & Graphics Seminar \| Talk @ XAI4CV Workshop
	Better Understanding Differences in Attribution Methods via Systematic Evaluations Sukrut Rao, Moritz Böhle, Bernt Schiele TPAMI, 2024 Paper \| Code \| Video
	Studying How to Efficiently and Effectively Guide Models with Explanations. Sukrut Rao, Moritz Böhle, Amin Parchami-Araghi, Bernt Schiele (equal contribution) ICCV, 2023* Paper \| Code \| Video
	Temperature Schedules for Self-Supervised Contrastive Methods on Long-Tail Data Anna Kukleva, Moritz Böhle, Bernt Schiele, Hilde Kuehne, Christian Rupprecht (equal contribution) ICLR, 2023* Paper \| Code
	B-cos Networks: Alignment is All We Need for Interpretability Moritz Böhle, Mario Fritz, Bernt Schiele CVPR, 2022 Paper \| Code \| Talk @ MIT Vision & Graphics Seminar \| Talk @ XAI4CV Workshop
	Towards Better Understanding Attribution Methods Sukrut Rao, Moritz Böhle, Bernt Schiele CVPR, 2022 Paper \| Code \| Video
	Optimising for Interpretability: Convolutional Dynamic Alignment Networks Moritz Böhle, Mario Fritz, Bernt Schiele TPAMI, 2022 Paper \| Code \| Video
	Convolutional Dynamic Alignment Networks for Interpretable Classifications Moritz Böhle, Mario Fritz, Bernt Schiele CVPR (oral), 2021 Paper \| Code \| Video
	Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification Moritz Böhle, Fabian Eitel, Martin Weygandt, Kerstin Ritter (equal contribution) Frontiers in Aging Neuroscience, 2019* Paper \| Code

Talks & Academic Activities

03/2023: Talk at Spotlight at the Explainability in Machine Learning Workshop, Tübingen
06/2022: Talk at Spotlight at the XAI for Computer Vision (XAI4CV) Workshop, CVPR
12/2021: Talk at Invited Talk at Vision & Graphics Seminar at the MIT

I am further proud to have served as a reviewer for IEEE PAMI, CVPR, ECCV, ICLR, NeurIPS, IEEE Trans. Inf. Forensics Secur., and ICML, and happy to have been named an outstanding reviewer for ECCV 2022!

Template stolen from Anna Kukleva who stole from Jon Barron :) Big thanks!