Importance-Driven Deep Learning System Testing

Importance-Driven Deep Learning System Testing

9 February 2020

Simos Gerasimou

Hasan Ferit Eniser

Papers citing "Importance-Driven Deep Learning System Testing"

19 / 19 papers shown

Title
Scope Compliance Uncertainty Estimate Al-Harith Farhad Ioannis Sorokos Mohammed Naveed Akram Koorosh Aslansefat Daniel Schneider 26 0 0 17 Dec 2023
DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks Zohreh Aghababaeyan Manel Abdellatif Mahboubeh Dadkhah Lionel C. Briand AAML 31 15 0 08 Mar 2023
Testing the Channels of Convolutional Neural Networks Kang Choi Donghyun Son Younghoon Kim Jiwon Seo 20 1 0 06 Mar 2023
Neuroevolutionary algorithms driven by neuron coverage metrics for semi-supervised classification Roberto Santana Ivan Hidalgo-Cenalmor Unai Garciarena A. Mendiburu Jose A. Lozano 27 2 0 05 Mar 2023
Mimose: An Input-Aware Checkpointing Planner for Efficient Training on GPU Jian-He Liao Mingzhen Li Qingxiao Sun Jiwei Hao F. Yu ... Ye Tao Zicheng Zhang Hailong Yang Zhongzhi Luan D. Qian 23 4 0 06 Sep 2022
EasyScale: Accuracy-consistent Elastic Training for Deep Learning Mingzhen Li Wencong Xiao Biao Sun Hanyu Zhao Hailong Yang ... Xianyan Jia Yi Liu Yong Li Wei Lin D. Qian 14 7 0 30 Aug 2022
Guiding the retraining of convolutional neural networks against adversarial inputs Francisco Durán Silverio Martínez-Fernández Michael Felderer Xavier Franch AAML 30 1 0 08 Jul 2022
Towards Training Reproducible Deep Learning Models Boyuan Chen Mingzhi Wen Yong Shi Dayi Lin Gopi Krishnan Rajbahadur Zhen Ming Z. Jiang SyDa 15 37 0 04 Feb 2022
Security for Machine Learning-based Software Systems: a survey of threats, practices and challenges Huaming Chen Muhammad Ali Babar AAML 34 21 0 12 Jan 2022
Black-Box Testing of Deep Neural Networks Through Test Case Diversity Zohreh Aghababaeyan Manel Abdellatif Lionel C. Briand Ramesh S M. Bagherzadeh AAML 37 44 0 20 Dec 2021
A Review and Refinement of Surprise Adequacy Michael Weiss Rwiddhi Chakraborty Paolo Tonella AAML AI4TS 16 16 0 10 Mar 2021
Machine Learning Model Development from a Software Engineering Perspective: A Systematic Literature Review Giuliano Lorenzoni Paulo S. C. Alencar Nathalia Nascimento Donald D. Cowan 16 18 0 15 Feb 2021
A Software Engineering Perspective on Engineering Machine Learning Systems: State of the Art and Challenges G. Giray 30 120 0 14 Dec 2020
DeepSmartFuzzer: Reward Guided Test Generation For Deep Learning Samet Demir Hasan Ferit Eniser A. Sen AAML 11 28 0 24 Nov 2019
DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks D. Gopinath Guy Katz C. Păsăreanu Clark W. Barrett AAML 42 87 0 02 Oct 2017
A Survey on Deep Learning in Medical Image Analysis G. Litjens Thijs Kooi B. Bejnordi A. Setio F. Ciompi Mohsen Ghafoorian Jeroen van der Laak Bram van Ginneken C. I. Sánchez OOD 295 10,618 0 19 Feb 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 231 1,837 0 03 Feb 2017
Safety Verification of Deep Neural Networks Xiaowei Huang M. Kwiatkowska Sen Wang Min Wu AAML 180 932 0 21 Oct 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 287 5,837 0 08 Jul 2016