Robustness Certification of Generative Models

Robustness Certification of Generative Models

30 April 2020

Martin Vechev

Papers citing "Robustness Certification of Generative Models"

8 / 8 papers shown

Title
Verification of Neural Networks against Convolutional Perturbations via Parameterised Kernels Benedikt Brückner Alessio Lomuscio AAML 52 0 0 07 Nov 2024
Learning from Uncertain Data: From Possible Worlds to Possible Models Jiongli Zhu Su Feng Boris Glavic Babak Salimi 27 0 0 28 May 2024
Mathematical Algorithm Design for Deep Learning under Societal and Judicial Constraints: The Algorithmic Transparency Requirement Holger Boche Adalbert Fono Gitta Kutyniok FaML 25 4 0 18 Jan 2024
Verifying Attention Robustness of Deep Neural Networks against Semantic Perturbations S. Munakata Caterina Urban Haruki Yokoyama Koji Yamamoto Kazuki Munakata AAML 15 4 0 13 Jul 2022
Latent Space Smoothing for Individually Fair Representations Momchil Peychev Anian Ruoss Mislav Balunović Maximilian Baader Martin Vechev FaML 36 18 0 26 Nov 2021
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications Leonard Berrada Sumanth Dathathri Krishnamurthy Dvijotham Robert Stanforth Rudy Bunel J. Uesato Sven Gowal M. P. Kumar AAML OOD 30 17 0 18 Feb 2021
Scaling Polyhedral Neural Network Verification on GPUs Christoph Müller F. Serre Gagandeep Singh Markus Püschel Martin Vechev AAML 15 56 0 20 Jul 2020
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 226 1,835 0 03 Feb 2017