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My research interest is machine learning with differentiable algorithms.
For example, I have made a general framework for making algorithms differentiable, and have also focussed on differentiable sorting and differentiable rendering.
While making algorithms differentiable by propagating distributions through them is a lot of fun, I also like propagating distributions through neural networks, which improves uncertainty estimation, robustness, and fairness.
I work in the Visual Computing Group (Prof. Oliver Deussen) at the University of Konstanz and in collaborations with Christian Borgelt, Hilde Kuehne, Mikhail Yurochkin, among others.
In the past, I was working, i.a., at TAU, DESY, PSI, and CERN.
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News
Oct 2021 Our papers "Learning with Algorithmic Supervision via Continuous Relaxations" and "Post-processing for Individual Fairness" were accepted to NeurIPS!
Oct 2021 Our paper "Style Agnostic 3D Reconstruction via Adversarial Style Transfer" was accepted to WACV.
May 2021 Our paper "Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision" was accepted to ICML.
Apr 2020 Presenting my research @ Brown University (Apr 20)
Mar 2020 Research stay @ University of Rhode Island
Feb 2020 Presenting my research @ FAIR and Google Research in Paris
Dec 2019 Presenting @ NeurIPS: AlgoNet: C∞ Smooth Algorithmic Neural Networks for Solving Inverse Problems
Nov 2019 Science Slams in Ulm (Nov 26) and in Reutlingen (Nov 27)
Nov 2019 I am honored to receive the Konrad-Zuse-Jugendpreis
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Research
My main research interests are algorithmic supervision, differentiable algorithms, and analytical propagation of distributions through neural networks.
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Learning with Algorithmic Supervision via Continuous Relaxations
Felix Petersen,
Christian Borgelt,
Hilde Kuehne,
Oliver Deussen
in Proceedings of the 35th International Conference on Neural Information Processing Systems (NeurIPS 2021)
YouTube Code / AlgoVision library
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Post-processing for Individual Fairness
Felix Petersen*,
Debarghya Mukherjee*,
Yuekai Sun,
Mikhail Yurochkin
in Proceedings of the 35th International Conference on Neural Information Processing Systems (NeurIPS 2021)
YouTube
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Style Agnostic 3D Reconstruction via Adversarial Style Transfer
Felix Petersen,
Hilde Kuehne,
Bastian Goldluecke,
Oliver Deussen
in Proceedings of the IEEE Winter Conf. on Applications of Computer Vision (WACV 2022)
YouTube
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Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision
Felix Petersen,
Christian Borgelt,
Hilde Kuehne,
Oliver Deussen
in Proceedings of the 38th International Conference on Machine Learning (ICML 2021)
YouTube
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AlgoNet: C∞ Smooth Algorithmic Neural Networks
Felix Petersen,
Christian Borgelt,
Oliver Deussen
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Pix2Vex: Image-to-Geometry Reconstruction using a Smooth Differentiable Renderer
Felix Petersen,
Amit H. Bermano,
Oliver Deussen,
Daniel Cohen-Or
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Towards Formula Translation using Recursive Neural Networks
Felix Petersen,
Moritz Schubotz,
Bela Gipp
in Proceedings of the 11th Conference on Intelligent Computer Mathematics (CICM), 2018
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LaTeXEqChecker
- A framework for checking mathematical semantics in LaTeX documents
Felix Petersen
Presented in the Special Session of the 11th Conference on Intelligent Computer Mathematics (CICM), 2018
Slides
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Maths Workshop + Individualized Maths Introduction
since WS 2019/2020
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Tutor: Analysis and Linear Algebra
SS 2019 + 2020 + 2021
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Seminar: Current Trends in Computer Graphics (+ Neural Networks, and Mathematical Language Processing)
WS 2019/2020
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Tutor: Discrete Mathematics
SS 2017
and SS 2018
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Tutor: Programming Course 1 (Java)
WS 2016/2017 and WS 2017/2018
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