Decorrelative Network Architecture for Robust Electrocardiogram Classification

19 July 2022

Papers citing "Decorrelative Network Architecture for Robust Electrocardiogram Classification"

32 / 32 papers shown

Title
A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap? Nathan G. Drenkow Numair Sani I. Shpitser Mathias Unberath 39 78 0 01 Dec 2021
Disrupting Adversarial Transferability in Deep Neural Networks Christopher Wiedeman Ge Wang AAML 86 7 0 27 Aug 2021
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles Huanrui Yang Jingyang Zhang Hongliang Dong Nathan Inkawhich Andrew B. Gardner Andrew Touchet Wesley Wilkes Heath Berry H. Li AAML 61 109 0 30 Sep 2020
Measuring Robustness to Natural Distribution Shifts in Image Classification Rohan Taori Achal Dave Vaishaal Shankar Nicholas Carlini Benjamin Recht Ludwig Schmidt OOD 113 546 0 01 Jul 2020
Universal Adversarial Perturbations: A Survey Ashutosh Chaubey Nikhil Agrawal Kavya Barnwal K. K. Guliani Pramod Mehta OOD AAML 77 47 0 16 May 2020
Bayesian Deep Learning and a Probabilistic Perspective of Generalization A. Wilson Pavel Izmailov UQCV BDL OOD 99 654 0 20 Feb 2020
The Case for Bayesian Deep Learning A. Wilson UQCV BDL OOD 112 112 0 29 Jan 2020
Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review linda Qiao Yuxi Zhou Junyuan Shang Cao Xiao Jimeng Sun 65 125 0 28 Dec 2019
Adversarial Robustness through Local Linearization Chongli Qin James Martens Sven Gowal Dilip Krishnan Krishnamurthy Dvijotham Alhussein Fawzi Soham De Robert Stanforth Pushmeet Kohli AAML 67 308 0 04 Jul 2019
A Fourier Perspective on Model Robustness in Computer Vision Dong Yin Raphael Gontijo-Lopes Jonathon Shlens E. D. Cubuk Justin Gilmer OOD 81 498 0 21 Jun 2019
DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks Aryan Mobiny H. Nguyen S. Moulik Naveen Garg Carol C. Wu UQCV BDL 54 161 0 07 Jun 2019
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran Aleksander Madry SILM 89 1,838 0 06 May 2019
Constructing Unrestricted Adversarial Examples with Generative Models Yang Song Rui Shu Nate Kushman Stefano Ermon GAN AAML 214 307 0 21 May 2018
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks J. Uesato Brendan O'Donoghue Aaron van den Oord Pushmeet Kohli AAML 150 604 0 15 Feb 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 219 3,186 0 01 Feb 2018
Generating Adversarial Examples with Adversarial Networks Chaowei Xiao Yue Liu Jun-Yan Zhu Warren He M. Liu D. Song GAN AAML 115 899 0 08 Jan 2018
High Dimensional Spaces, Deep Learning and Adversarial Examples S. Dube 65 29 0 02 Jan 2018
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey Naveed Akhtar Ajmal Mian AAML 95 1,867 0 02 Jan 2018
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 304 12,069 0 19 Jun 2017
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation Matthias Hein Maksym Andriushchenko AAML 110 511 0 23 May 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 121 1,857 0 20 May 2017
The Space of Transferable Adversarial Examples Florian Tramèr Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML SILM 90 557 0 11 Apr 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 831 5,821 0 05 Dec 2016
Defensive Distillation is Not Robust to Adversarial Examples Nicholas Carlini D. Wagner 56 338 0 14 Jul 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 540 5,897 0 08 Jul 2016
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples Nicolas Papernot Patrick McDaniel Ian Goodfellow SILM AAML 112 1,739 0 24 May 2016
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 151 4,895 0 14 Nov 2015
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks Nicolas Papernot Patrick McDaniel Xi Wu S. Jha A. Swami AAML 99 3,072 0 14 Nov 2015
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding Alex Kendall Vijay Badrinarayanan R. Cipolla UQCV BDL 86 1,065 0 09 Nov 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 821 9,318 0 06 Jun 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 277 19,049 0 20 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 270 14,918 1 21 Dec 2013