The troublesome kernel -- On hallucinations, no free lunches and the accuracy-stability trade-off in inverse problems

5 January 2020

Papers citing "The troublesome kernel -- On hallucinations, no free lunches and the accuracy-stability trade-off in inverse problems"

22 / 22 papers shown

Title
Localized adversarial artifacts for compressed sensing MRI Rima Alaifari Giovanni S. Alberti Tandri Gauksson AAML 46 4 0 10 Jun 2022
SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation Arjun D Desai Andrew M Schmidt E. Rubin Christopher M. Sandino Marianne S. Black ... R. Boutin Christopher Ré G. Gold B. Hargreaves Akshay S. Chaudhari 53 65 0 14 Mar 2022
Limitations of Deep Learning for Inverse Problems on Digital Hardware Holger Boche Adalbert Fono Gitta Kutyniok 47 25 0 28 Feb 2022
Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI Thomas Yu T. Hilbert G. Piredda Arun A. Joseph G. Bonanno ... P. Omoumi Meritxell Bach Cuadra Erick Jorge Canales-Rodríguez Thomas Kober Jean-Philippe Thiran 41 5 0 29 Jan 2022
Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstruction D. Karkalousos S. Noteboom H. Hulst F. Vos M. Caan OOD AI4CE 44 10 0 30 Nov 2021
The mathematics of adversarial attacks in AI -- Why deep learning is unstable despite the existence of stable neural networks Alexander Bastounis A. Hansen Verner Vlacic AAML OOD 51 28 0 13 Sep 2021
Can stable and accurate neural networks be computed? -- On the barriers of deep learning and Smale's 18th problem Matthew J. Colbrook Vegard Antun A. Hansen 89 133 0 20 Jan 2021
Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data Ben Adcock Simone Brugiapaglia N. Dexter S. Moraga 64 29 0 11 Dec 2020
Model Adaptation for Inverse Problems in Imaging Davis Gilton Greg Ongie Rebecca Willett OOD MedIm 57 48 0 30 Nov 2020
Solving Inverse Problems With Deep Neural Networks -- Robustness Included? Martin Genzel Jan Macdonald M. März AAML OOD 43 103 0 09 Nov 2020
XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge Zaccharie Ramzi P. Ciuciu Jean-Luc Starck 31 23 0 15 Oct 2020
Robust Compressed Sensing using Generative Models A. Jalal Liu Liu A. Dimakis Constantine Caramanis 41 39 0 16 Jun 2020
Deep Learning Techniques for Inverse Problems in Imaging Greg Ongie A. Jalal Christopher A. Metzler Richard G. Baraniuk A. Dimakis Rebecca Willett 38 526 0 12 May 2020
Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning S. Ravishankar J. C. Ye Jeffrey A. Fessler 40 240 0 04 Apr 2019
Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction Florian Knoll Kerstin Hammernik Chi Zhang S. Moeller Thomas Pock D. Sodickson Mehmet Akçakaya OOD 53 264 0 01 Apr 2019
On instabilities of deep learning in image reconstruction - Does AI come at a cost? Vegard Antun F. Renna C. Poon Ben Adcock A. Hansen 35 601 0 14 Feb 2019
Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks Reinhard Heckel Paul Hand 55 284 0 02 Oct 2018
Modeling Sparse Deviations for Compressed Sensing using Generative Models Manik Dhar Aditya Grover Stefano Ermon 23 79 0 04 Jul 2018
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text Nicholas Carlini D. Wagner AAML 53 1,076 0 05 Jan 2018
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems E. Weinan Ting Yu 83 1,373 0 30 Sep 2017
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems Tim Meinhardt Michael Möller C. Hazirbas Daniel Cremers 51 357 0 11 Apr 2017
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 108 14,831 1 21 Dec 2013