CLIP: Cheap Lipschitz Training of Neural Networks

23 March 2021

Papers citing "CLIP: Cheap Lipschitz Training of Neural Networks"

23 / 23 papers shown

Title
Deep End-to-End Posterior ENergy (DEEPEN) for image recovery Jyothi Rikhab Chand M. Jacob DiffM 41 0 0 21 Mar 2025
A Tunable Despeckling Neural Network Stabilized via Diffusion Equation Yi Ran Zhichang Guo Jia Li Yao Li Martin Burger Boying Wu DiffM 63 0 0 24 Nov 2024
Invertible ResNets for Inverse Imaging Problems: Competitive Performance with Provable Regularization Properties Clemens Arndt Judith Nickel 31 0 0 20 Sep 2024
Consistency of Neural Causal Partial Identification Jiyuan Tan Jose Blanchet Vasilis Syrgkanis CML 32 0 0 24 May 2024
Your Network May Need to Be Rewritten: Network Adversarial Based on High-Dimensional Function Graph Decomposition Xiaoyan Su Yinghao Zhu Run Li AAML 24 0 0 04 May 2024
Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space Leo Schwinn David Dobre Sophie Xhonneux Gauthier Gidel Stephan Gunnemann AAML 51 36 0 14 Feb 2024
Local monotone operator learning using non-monotone operators: MnM-MOL Maneesh John Jyothi Rikabh Chand Mathews Jacob 16 1 0 01 Dec 2023
Raising the Bar for Certified Adversarial Robustness with Diffusion Models Thomas Altstidl David Dobre Björn Eskofier Gauthier Gidel Leo Schwinn DiffM 25 7 0 17 May 2023
Accelerated parallel MRI using memory efficient and robust monotone operator learning (MOL) Aniket Pramanik M. Jacob 19 2 0 03 Apr 2023
Some Fundamental Aspects about Lipschitz Continuity of Neural Networks Grigory Khromov Sidak Pal Singh 24 7 0 21 Feb 2023
Plug-and-Play Deep Energy Model for Inverse problems Jyothi Rikabh Chand M. Jacob 16 0 0 15 Feb 2023
Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions Stanislas Ducotterd Alexis Goujon Pakshal Bohra Dimitris Perdios Sebastian Neumayer M. Unser 35 12 0 28 Oct 2022
Learning Globally Smooth Functions on Manifolds J. Cerviño Luiz F. O. Chamon B. Haeffele René Vidal Alejandro Ribeiro 27 5 0 01 Oct 2022
Memory-efficient model-based deep learning with convergence and robustness guarantees Aniket Pramanik M. Zimmerman M. Jacob 3DV 11 13 0 06 Jun 2022
Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification Leo Schwinn Leon Bungert A. Nguyen René Raab Falk Pulsmeyer Doina Precup Björn Eskofier Dario Zanca OOD 42 12 0 19 May 2022
Approximation of Lipschitz Functions using Deep Spline Neural Networks Sebastian Neumayer Alexis Goujon Pakshal Bohra M. Unser 21 15 0 13 Apr 2022
A Quantitative Geometric Approach to Neural-Network Smoothness Z. Wang Gautam Prakriya S. Jha 35 13 0 02 Mar 2022
The Geometry of Adversarial Training in Binary Classification Leon Bungert Nicolas García Trillos Ryan W. Murray AAML 22 22 0 26 Nov 2021
Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods Tobias Alt Karl Schrader Joachim Weickert Pascal Peter M. Augustin 22 4 0 31 Aug 2021
Connections between Numerical Algorithms for PDEs and Neural Networks Tobias Alt Karl Schrader M. Augustin Pascal Peter Joachim Weickert PINN 18 21 0 30 Jul 2021
Exploring Misclassifications of Robust Neural Networks to Enhance Adversarial Attacks Leo Schwinn René Raab A. Nguyen Dario Zanca Bjoern M. Eskofier AAML 14 58 0 21 May 2021
Learning Lipschitz Feedback Policies from Expert Demonstrations: Closed-Loop Guarantees, Generalization and Robustness Abed AlRahman Al Makdah Vishaal Krishnan Fabio Pasqualetti 22 0 0 30 Mar 2021
Optimization with learning-informed differential equation constraints and its applications Guozhi Dong M. Hintermueller Kostas Papafitsoros PINN 19 14 0 25 Aug 2020