Towards Formula Translation using Recursive Neural Networks. - Felix Petersen; Moritz Schubotz; Bela Gipp (CICM Workshops, 2018)

Pix2Vex: Image-to-Geometry Reconstruction using a Smooth Differentiable Renderer. - Felix Petersen; Amit H. Bermano; Oliver Deussen; Daniel Cohen-Or (CoRR, 2019)

AlgoNet: C∞ Smooth Algorithmic Neural Networks. - Felix Petersen; Christian Borgelt; Oliver Deussen (CoRR, 2019)

Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision. - Felix Petersen; Christian Borgelt; Hilde Kuehne; Oliver Deussen (ICML, 2021)

Learning with Algorithmic Supervision via Continuous Relaxations. - Felix Petersen; Christian Borgelt; Hilde Kuehne; Oliver Deussen (NeurIPS, 2021)

Post-processing for Individual Fairness. - Felix Petersen; Debarghya Mukherjee; Yuekai Sun; Mikhail Yurochkin (NeurIPS, 2021)

GenDR: A Generalized Differentiable Renderer. - Felix Petersen; Bastian Goldluecke; Christian Borgelt; Oliver Deussen (CVPR, 2022)

Monotonic Differentiable Sorting Networks. - Felix Petersen; Christian Borgelt; Hilde Kuehne; Oliver Deussen (ICLR, 2022)

Differentiable Top-k Classification Learning. - Felix Petersen; Hilde Kuehne; Christian Borgelt; Oliver Deussen (ICML, 2022)

Domain Adaptation meets Individual Fairness. And they get along. - Debarghya Mukherjee; Felix Petersen; Mikhail Yurochkin; Yuekai Sun (NeurIPS, 2022)

Deep Differentiable Logic Gate Networks. - Felix Petersen; Christian Borgelt; Hilde Kuehne; Oliver Deussen (NeurIPS, 2022)

Style Agnostic 3D Reconstruction via Adversarial Style Transfer. - Felix Petersen; Bastian Goldluecke; Oliver Deussen; Hilde Kuehne (WACV, 2022)

Learning with Differentiable Algorithms. - Felix Petersen (CoRR, 2022)

Learning with Differentiable Algorithms. - Felix Petersen (2022)

Lernen mit differenzierbaren Algorithmen. - Felix Petersen (Ausgezeichnete Informatikdissertationen, 2022)

Neural Machine Translation for Mathematical Formulae. - Felix Petersen; Moritz Schubotz; André Greiner-Petter; Bela Gipp (ACL (1), 2023)

Learning by Sorting: Self-supervised Learning with Group Ordering Constraints. - Nina Shvetsova; Felix Petersen; Anna Kukleva; Bernt Schiele; Hilde Kuehne (ICCV, 2023)

ISAAC Newton: Input-based Approximate Curvature for Newton's Method. - Felix Petersen; Tobias Sutter; Christian Borgelt; Dongsung Huh; Hilde Kuehne; Yuekai Sun; Oliver Deussen (ICLR, 2023)

Grounding Everything: Emerging Localization Properties in Vision-Language Transformers. - Walid Bousselham; Felix Petersen; Vittorio Ferrari; Hilde Kuehne (CVPR, 2024)

Uncertainty Quantification via Stable Distribution Propagation. - Felix Petersen; Aashwin Ananda Mishra; Hilde Kuehne; Christian Borgelt; Oliver Deussen; Mikhail Yurochkin (ICLR, 2024)

TrAct: Making First-layer Pre-Activations Trainable. - Felix Petersen; Christian Borgelt; Stefano Ermon (NeurIPS, 2024)

Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms. - Felix Petersen; Christian Borgelt; Tobias Sutter; Hilde Kuehne; Oliver Deussen; Stefano Ermon (NeurIPS, 2024)

Convolutional Differentiable Logic Gate Networks. - Felix Petersen; Hilde Kuehne; Christian Borgelt; Julian Welzel; Stefano Ermon (NeurIPS, 2024)

CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion. - Joshua Kazdan; Hao Sun; Jiaqi Han; Felix Petersen; Stefano Ermon (CoRR, 2024)

Generalizing Stochastic Smoothing for Differentiation and Gradient Estimation. - Felix Petersen; Christian Borgelt; Aashwin Ananda Mishra; Stefano Ermon (CoRR, 2024)

CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion. - Joshua Kazdan; Hao Sun; Jiaqi Han; Felix Petersen; Frederick Vu; Stefano Ermon (ICLR, 2025)