Felix Petersen
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My primary research interest is differentiable relaxations in machine learning. In particular, I'm interested in differentiable logic gate networks for extremely efficient inference, differentiable sorting (networks) for weakly-supervised learning, differentiable rendering, as well as general stochastic gradient estimation and optimization.

Currently, I am a postdoctoral researcher at Stanford University in Stefano Ermon's group.

Previously, I have been working, i.a., at the University of Konstanz, at TAU, DESY, PSI, and CERN.

News

Sep 2024 We have 3 accepted papers at NeurIPS 2024, one of which is an oral! 🎊

Sep 2024 Presenting my research at MIT-IBM, Harvard, Boston University, and CMU.

Apr 2024 Our second Differentiable Almost Everything workshop has been accepted for ICML 2024 🎻!

Mar 2024 Our paper "Grounding Everything: Emerging Localization Properties in Vision-Language Transformers" was accepted to CVPR 2024! Congrats to Walid!

Jan 2024 Our paper "Uncertainty Quantification via Stable Distribution Propagation" was accepted to ICLR 2024!

Research

The focus of my research lies in differentiable relaxations of non-differentiable operations in machine learning. Differentiable relaxations enable a plethora of optimization tasks: from optimizing logic gate networks [1] and optimizing through the 3D rendering pipeline [2, 3, 4] to differentiating sorting and ranking [5, 6] for supervised [7] and self-supervised [8] learning. Beyond differentiable algorithms, this branches out into various domains including stochastic gradient estimation [9], analytical distribution propagation [10], second-order optimization [9, 11], uncertainty quantification [10], fairness [12, 13], and efficient neural architectures [1, 14].



Convolutional Differentiable Logic Gate Networks
Felix Petersen, Hilde Kuehne, Christian Borgelt, Julian Welzel, Stefano Ermon
in Proceedings of the 38th International Conference on Neural Information Processing Systems (NeurIPS 2024) (Oral)

Code (will be updated soon)


TrAct: Making First-layer Pre-Activations Trainable
Felix Petersen, Christian Borgelt, Stefano Ermon
in Proceedings of the 38th International Conference on Neural Information Processing Systems (NeurIPS 2024)


Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms
Felix Petersen, Christian Borgelt, Tobias Sutter, Hilde Kuehne, Oliver Deussen, Stefano Ermon
in Proceedings of the 38th International Conference on Neural Information Processing Systems (NeurIPS 2024)


Generalizing Stochastic Smoothing for Differentiation and Gradient Estimation
Felix Petersen, Christian Borgelt, Aashwin Mishra, Stefano Ermon
arXiv preprint


Grounding Everything: Emerging Localization Properties in Vision-Language Transformers
Walid Bousselham, Felix Petersen, Vittorio Ferrari, Hilde Kuehne
in Proc. of the Conference on Computer Vision and Pattern Recognition (CVPR 2024)

Demo      Code


Uncertainty Quantification via Stable Distribution Propagation
Felix Petersen, Aashwin Mishra, Hilde Kuehne, Christian Borgelt, Oliver Deussen, Mikhail Yurochkin
in Proc. of the International Conference on Learning Representations (ICLR 2024)


Learning by Sorting: Self-supervised Learning with Group Ordering Constraints
Nina Shvetsova, Felix Petersen, Anna Kukleva, Bernt Schiele, Hilde Kuehne
in Proc. of the International Conference on Computer Vision (ICCV 2023)

Code


Neural Machine Translation for Mathematical Formulae
Felix Petersen, Moritz Schubotz, Andre Greiner-Petter, Bela Gipp
in Proc. of the 61st Annual Meeting of the Association for Computational Linguistics (ACL 2023)

YouTube


ISAAC Newton: Input-based Approximate Curvature for Newton's Method
Felix Petersen, Tobias Sutter, Christian Borgelt, Dongsung Huh, Hilde Kuehne, Yuekai Sun, Oliver Deussen
in Proc. of the International Conference on Learning Representations (ICLR 2023)

YouTube      Code


Deep Differentiable Logic Gate Networks
Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen
in Proceedings of the 36th International Conference on Neural Information Processing Systems (NeurIPS 2022)

Code


Domain Adaptation meets Individual Fairness. And they get along.
Debarghya Mukherjee*, Felix Petersen*, Mikhail Yurochkin, Yuekai Sun
in Proceedings of the 36th International Conference on Neural Information Processing Systems (NeurIPS 2022)


Learning with Differentiable Algorithms
Felix Petersen
PhD thesis (summa cum laude), University of Konstanz


Differentiable Top-k Classification Learning
Felix Petersen, Hilde Kuehne, Christian Borgelt, Oliver Deussen
in Proceedings of the 39th International Conference on Machine Learning (ICML 2022)

YouTube      Code


GenDR: A Generalized Differentiable Renderer
Felix Petersen, Christian Borgelt, Bastian Goldluecke, Oliver Deussen
in Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR 2022)

YouTube      Code


Monotonic Differentiable Sorting Networks
Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen
in Proceedings of the International Conference on Learning Representations (ICLR 2022)

YouTube      Code / diffsort library

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



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



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      Code


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



AlgoNet: C Smooth Algorithmic Neural Networks
Felix Petersen, Christian Borgelt, Oliver Deussen

Pix2Vex: Image-to-Geometry Reconstruction using a Smooth Differentiable Renderer
Felix Petersen, Amit H. Bermano, Oliver Deussen, Daniel Cohen-Or
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
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

Media Coverage
campus_kn

campus.kn article about my Schülerstudium (studying while in school):
English: “At university, knowledge counts more than age” – “Im Gespräch” interviews Felix Petersen, who completed a Schülerstudium
German: „An der Uni ist die Expertise wichtiger als das Alter“ – Im Gespräch mit Schülerstudium-Absolvent Felix Petersen


suedkurier-Aug27-2019

Südkurier article about me as a 19 years old PhD student: Dieser junge Mann ist 19 Jahre alt – und schreibt gerade seine Doktorarbeit. Über ein hochbegabtes Talent der Konstanzer Uni


jufo_TV_Feb14-2019

German TV: Short interview about my participation at Jugend forscht: „Jugend forscht“: Hamburger Schüler stellen Forschungsprojekte vor


suedkurier

Südkurier article about my work at DESY: Streng geheimes Forschungsprojekt: 17-jähriger Informatik-Student entwickelt neuartigen Röntgenlaser



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