Felix Petersen

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Oliver DeussenProfessor of Computer Science, University of Konstanz Verified email at uni-konstanz.de
Christian BorgeltProfessor for Data Science, Paris Lodron University of SalzburgVerified email at borgelt.net
Hilde KuehneTuebingen AI Center, University of Tuebingen, MIT-IBM Watson LabVerified email at uni-tuebingen.de
Yuekai SunUniversity of MichiganVerified email at umich.edu
Mikhail YurochkinResearch Manager, Statistical Large Language Modeling, IBM Research and MIT-IBM Watson AI LabVerified email at umich.edu
Debarghya MukherjeeAssistant Professor, Boston UniversityVerified email at umich.edu
Bastian GoldlueckeProfessor, University of KonstanzVerified email at uni-konstanz.de
Daniel Cohen-OrProfessor of Computer Science, Tel Aviv UniversityVerified email at tau.ac.il
Amit Haim BermanoAssociate Professor, Tel-Aviv University, Computer ScienceVerified email at tauex.tau.ac.il
Stefano ErmonStanford UniversityVerified email at cs.stanford.edu
Walid BousselhamUniversity of BonnVerified email at em.uni-frankfurt.de
Vittorio FerrariDirector of science at SynthesiaVerified email at google.com
Tobias SutterUniversity of KonstanzVerified email at uni-konstanz.de
Bela GippProfessor at the University of Göttingen & Director of Research at the SUB GöttingenVerified email at uni-goettingen.de
Moritz SchubotzNational Institute of Informatics, JapanVerified email at nii.ac.jp
Anna KuklevaMax Planck Institute for InformaticsVerified email at mpi-inf.mpg.de
Nina ShvetsovaUniversity of BonnVerified email at uni-frankfurt.de
Bernt SchieleProfessor, Max Planck Institute for Informatics, Saarland University, Saarland Informatics CampusVerified email at mpi-inf.mpg.de
Andre Greiner-PetterUniversity of GöttingenVerified email at zbmath.org
Joshua KazdanPhD Candidate in Statistics, Stanford UniversityVerified email at stanford.edu

Felix Petersen

Stanford University, University of Konstanz

Verified email at stanford.edu - Homepage

Differentiable Algorithms Neural Networks Machine Learning


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Post-processing for Individual Fairness F Petersen, D Mukherjee, Y Sun, M Yurochkin Advances in Neural Information Processing Systems 34, 25944-25955, 2021	95	2021
Pix2Vex: Image-to-Geometry Reconstruction using a Smooth Differentiable Renderer F Petersen, AH Bermano, O Deussen, D Cohen-Or arXiv preprint arXiv:1903.11149, 2019	61	2019
Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision F Petersen, C Borgelt, H Kuehne, O Deussen International Conference on Machine Learning 2021, 2021	39	2021
Differentiable Top-k Classification Learning F Petersen, H Kuehne, C Borgelt, O Deussen International Conference on Machine Learning, 17656-17668, 2022	34	2022
Monotonic Differentiable Sorting Networks F Petersen, C Borgelt, H Kuehne, O Deussen International Conference on Learning Representations, 2022	31	2022
Learning with Algorithmic Supervision via Continuous Relaxations F Petersen, C Borgelt, H Kuehne, O Deussen Advances in Neural Information Processing Systems 34, 16520-16531, 2021	31	2021
Deep Differentiable Logic Gate Networks F Petersen, C Borgelt, H Kuehne, O Deussen Advances in Neural Information Processing Systems (NeurIPS), 2022	30	2022
GenDR: A Generalized Differentiable Renderer F Petersen, B Goldluecke, C Borgelt, O Deussen Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2022	26	2022
Domain Adaptation meets Individual Fairness. And they get along D Mukherjee, F Petersen, M Yurochkin, Y Sun Advances in Neural Information Processing Systems 35, 2022	22	2022
Grounding Everything: Emerging Localization Properties in Vision-Language Transformers W Bousselham, F Petersen, V Ferrari, H Kuehne IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024	21	2024
Learning with Differentiable Algorithms F Petersen arXiv preprint arXiv:2209.00616, 2022	16	2022
Learning by Sorting: Self-supervised Learning with Group Ordering Constraints N Shvetsova, F Petersen, A Kukleva, B Schiele, H Kuehne International Conference on Computer Vision (ICCV), 2023	11	2023
Style Agnostic 3D Reconstruction via Adversarial Style Transfer F Petersen, B Goldluecke, O Deussen, H Kuehne Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022	8	2022
ISAAC Newton: Input-based Approximate Curvature for Newton's Method F Petersen, T Sutter, C Borgelt, D Huh, H Kuehne, Y Sun, O Deussen International Conference on Learning Representations, 2023	7	2023
AlgoNet: Smooth Algorithmic Neural Networks F Petersen, C Borgelt, O Deussen arXiv preprint arXiv:1905.06886, 2019	6	2019
Towards Formula Translation using Recursive Neural Networks F Petersen, M Schubotz, B Gipp International Conference on Intelligent Computer Mathematics (CICM), 2018	4	2018
Uncertainty Quantification via Stable Distribution Propagation F Petersen, A Mishra, H Kuehne, C Borgelt, O Deussen, M Yurochkin International Conference on Learning Representations 2024, 2024	3	2024
Neural Machine Translation for Mathematical Formulae F Petersen, M Schubotz, A Greiner-Petter, B Gipp Association for Computational Linguistics (ACL) Main Conference Paper, 2023	3	2023
Newton losses: Using curvature information for learning with differentiable algorithms F Petersen, C Borgelt, T Sutter, H Kuehne, O Deussen, S Ermon arXiv preprint arXiv:2410.19055, 2024	1	2024
Generalizing stochastic smoothing for differentiation and gradient estimation F Petersen, C Borgelt, A Mishra, S Ermon arXiv preprint arXiv:2410.08125, 2024	1	2024

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