Fetching the paper…
Reading the bibliography…
Test Input Prioritizers (TIP) for Deep Neural Networks (DNN) are an important technique to handle the typically very large test datasets efficiently, saving computation and labeling costs.
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Dan Hendrycks and Thomas Dietterich. 2018 · 1903
Earlier work this paper cites.
MNIST-C: A Robustness Benchmark for Computer Vision
Norman Mu and Justin Gilmer. 2019 · 1906
Earlier work this paper cites.
On the generalized distance in statistics. National Institute of Science of India
Prasanta Chandra Mahalanobis. 1936 · 1936
Earlier work this paper cites.
Binary codes capable of correcting deletions, insertions, and reversals. In Soviet physics doklady , Vol. 10. Soviet Union, 707–710
Vladimir I Levenshtein et al · 1966
Earlier work this paper cites.
Elements of reusable object-oriented software . Vol. 99
Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides, and Design Patterns. 1995 · 1995
Earlier work this paper cites.
Prioritizing test cases for regression testing. In Proceedings of the International Symposium on Software Testing and Analysis, ISSTA . 102–112
Sebastian G. Elbaum, Alexey G. Malishevsky, and Gregg Rothermel. 2000 · 2000
Earlier work this paper cites.
Prioritizing Test Cases For Regression Testing
Gregg Rothermel, Roland H. Untch, Chengyun Chu, and Mary Jean Harrold. 2001 · 2001
Earlier work this paper cites.
Numerically stable, single-pass, parallel statistics algorithms. In 2009 IEEE International Conference on Cluster Computing and Workshops . IEEE, 1–8
Janine Bennett, Ray Grout, Philippe Pébay, Diana Roe, and David Thompson. 2009 · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky. 2009 · 2009
Earlier work this paper cites.
MNIST handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges. 2010 · 2010
Earlier work this paper cites.
Scikit-learn: Machine Learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011 · 2011
Earlier work this paper cites.
Kernel Density Estimation, Kernel Methods, and Fast Learning in Large Data Sets
Shitong Wang, Jun Wang, and Fu-lai Chung. 2014 · 2012
Earlier work this paper cites.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In Proceedings of the IEEE conference on computer vision and pattern recognition . 427–436
Anh Nguyen, Jason Yosinski, and Jeff Clune. 2015 · 2015
Earlier work this paper cites.
Uncertainty in Deep Learning
Yarin Gal. 2016 · 2016
Earlier work this paper cites.
Dropout As a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. In Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48 (New York, NY, USA) (ICML’16) . JMLR.org, 1050–1059
Yarin Gal and Zoubin Ghahramani. 2016 · 2016
Earlier work this paper cites.
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Dan Hendrycks and Kevin Gimpel. 2016 · 2016
Cited alongside, same era.
Deep bayesian active learning with image data. In International Conference on Machine Learning . PMLR, 1183–1192
Yarin Gal, Riashat Islam, and Zoubin Ghahramani. 2017 · 2017
Cited alongside, same era.
Deepxplore: Automated whitebox testing of deep learning systems. In proceedings of the 26th Symposium on Operating Systems Principles . 1–18
Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana. 2017 · 2017
Cited alongside, same era.
Jonas Rauber, Wieland Brendel, and Matthias Bethge. 2017 · 2017
Cited alongside, same era.
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Testing machine learning based systems: a systematic mapping
Vincenzo Riccio, Gunel Jahangiroba, Andrea Stocco, Nargiz Humbatova, Michael Weiss, and Paolo Tonella. 2020 · 2020
Later among the works it cites.
Towards anomaly detectors that learn continuously. In 2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) . IEEE, 201–208
Andrea Stocco and Paolo Tonella. 2020 · 2020
Later among the works it cites.
Misbehaviour Prediction for Autonomous Driving Systems. In Proceedings of 42nd International Conference on Software Engineering . ACM, 12 pages
Andrea Stocco, Michael Weiss, Marco Calzana, and Paolo Tonella. 2020 · 2020
Later among the works it cites.
Towards characterizing adversarial defects of deep learning software from the lens of uncertainty. In Proceedings of 42nd International Conference on Software Engineering . ACM
Xiyue Zhang, Xiaofei Xie, Lei Ma, Xiaoning Du, Qiang Hu, Yang Liu, Jianjun Zhao, and Meng Sun. 2020 · 2020
Later among the works it cites.
ACM Artifact Review and Badging – Version 2.0
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Han Xiao, Kashif Rasul, and Roland Vollgraf. 2017 · 2017
Cited alongside, same era.
Redefining prioritization: continuous prioritization for continuous integration. In Proceedings of the 40th International Conference on Software Engineering, ICSE . 688–698
Jingjing Liang, Sebastian G. Elbaum, and Gregg Rothermel. 2018 · 2018
Cited alongside, same era.
Deepgauge: Multi-granularity testing criteria for deep learning systems. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering . 120–131
Lei Ma, Felix Juefei-Xu, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Chunyang Chen, Ting Su, Li Li, Yang Liu, et al · 2018
Cited alongside, same era.
Numerically stable parallel computation of (co-) variance. In Proceedings of the 30th International Conference on Scientific and Statistical Database Management . 1–12
Erich Schubert and Michael Gertz. 2018 · 2018
Cited alongside, same era.
Guiding deep learning system testing using surprise adequacy. In 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) . IEEE, 1039–1049
Jinhan Kim, Robert Feldt, and Shin Yoo. 2019 · 2019
Cited alongside, same era.
Can you trust your models uncertainty? Evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D. Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek. 2019 · 2019
Cited alongside, same era.
Cats are not fish: Deep learning testing calls for out-of-distribution awareness. In Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering . 1041–1052
David Berend, Xiaofei Xie, Lei Ma, Lingjun Zhou, Yang Liu, Chi Xu, and Jianjun Zhao. 2020 · 2020
Cited alongside, same era.
Deepgini: prioritizing massive tests to enhance the robustness of deep neural networks. In Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis . 177–188
Yang Feng, Qingkai Shi, Xinyu Gao, Jun Wan, Chunrong Fang, and Zhenyu Chen. 2020 · 2020
Cited alongside, same era.
[n. d.] · 2021
Later among the works it cites.
ReScience C What’s the difference between replication and reproduction?
[n. d.] · 2021
Later among the works it cites.
Multimodal Surprise Adequacy Analysis of Inputs for Natural Language Processing DNN Models. In 2021 IEEE/ACM International Conference on Automation of Software Test (AST) . IEEE Computer Society, Los Alamitos, CA, USA, 80–89
S. Kim and S. Yoo. 2021 · 2021
Later among the works it cites.
Datasets: A Community Library for Natural Language Processing. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations . Association for Computational Linguistics, 175–184
Quentin Lhoest, Albert Villanova del Moral, Patrick von Platen, Thomas Wolf, Mario Šaško, Yacine Jernite, Abhishek Thakur, Lewis Tunstall, Suraj Patil, Mariama Drame, Julien Chaumond, Julien Plu, Joe Davison, Simon Brandeis, Victor Sanh, Teven Le Scao, Kevin Canwen Xu, Nicolas Patry, Steven Liu, Angelina McMillan-Major, Philipp Schmid, Sylvain Gugger, Nathan Raw, Sylvain Lesage, Anton Lozhkov, Matthew Carrigan, Théo Matussière, Leandro von Werra, Lysandre Debut, Stas Bekman, and Clément Delangue. 2021 · 2021
Later among the works it cites.
Test selection for deep learning systems
Wei Ma, Mike Papadakis, Anestis Tsakmalis, Maxime Cordy, and Yves Le Traon. 2021 · 2021
Later among the works it cites.
Virtuous Circle of AI. Lecture Slides, CS299 - Deep Learning, Stanford University
Andrew Ng. [n. d.] · 2021
Later among the works it cites.
ISSTA 2022 - Technical Track Call for Papers - Section on Replicability Studies
Yannis Smaragdakis. [n. d.] · 2021
Later among the works it cites.
A Review and Refinement of Surprise Adequacy. In 2021 IEEE/ACM Third International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest) . IEEE Computer Society, Los Alamitos, CA, USA, 17–24
Michael Weiss, Rwiddhi Chakraborty, and Paolo Tonella. 2021 · 2021
Later among the works it cites.
Fail-safe execution of deep learning based systems through uncertainty monitoring. In 2021 IEEE 14th International Conference on Software Testing, Validation and Verification (ICST). IEEE . IEEE, 24–35
Michael Weiss and Paolo Tonella. 2021a · 2021
Later among the works it cites.
Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification. In 2021 14th IEEE Conference on Software Testing, Verification and Validation (ICST) . 436–441
Michael Weiss and Paolo Tonella. 2021b · 2021
Later among the works it cites.