Fetching the paper…
Reading the bibliography…
Recently, neural networks have spread into numerous fields including many safety-critical systems.
Search Based Repair of Deep Neural Networks
Jeongju Sohn, Sungmin Kang, and Shin Yoo. 2019 · 1912
Earlier work this paper cites.
Logic, programming and Prolog
Ulf Nilsson and Jan Małuszyński. 1990 · 1990
Earlier work this paper cites.
Elements of Style: Analyzing a Software Design Feature with a Counterexample Detector. In Proceedings of the 1996 International Symposium on Software Testing and Analysis, ISSTA 1996, San Diego, CA, USA, January 8-10, 1996 . ACM, 239–249
Daniel Jackson and Craig Damon. 1996 · 1996
Earlier work this paper cites.
SOCRATES: Towards a Unified Platform for Neural Network Verification
Long H. Pham, Jiaying Li, and Jun Sun. 2020 · 2007
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Search Based Software Engineering: Techniques, Taxonomy, Tutorial. In Empirical Software Engineering and Verification - International Summer Schools, LASER 2008-2010, Elba Island, Italy, Revised Tutorial Lectures (Lecture Notes in Computer Science, Vol. 7007) , Bertrand Meyer and Martin Nordio (Eds.). Springer, 1–59
Mark Harman, Phil McMinn, Jerffeson Teixeira de Souza, and Shin Yoo. 2010 · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng. 2011 · 2011
Earlier work this paper cites.
An Empirical Study of Bugs in Machine Learning Systems. In 23rd IEEE International Symposium on Software Reliability Engineering, ISSRE 2012, Dallas, TX, USA, November 27-30, 2012 . IEEE Computer Society, 271–280
Ferdian Thung, Shaowei Wang, David Lo, and Lingxiao Jiang. 2012 · 2012
Earlier work this paper cites.
Deep learning in neural networks: An overview
Jürgen Schmidhuber. 2015 · 2014
Earlier work this paper cites.
Validating a Deep Learning Framework by Metamorphic Testing. In 2nd IEEE/ACM International Workshop on Metamorphic Testing, MET@ICSE 2017, Buenos Aires, Argentina, May 22, 2017 . IEEE Computer Society, 28–34
Junhua Ding, Xiaojun Kang, and Xin-Hua Hu. 2017 · 2017
Earlier work this paper cites.
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf. 2017 · 2017
Earlier work this paper cites.
Neural Architecture Search with Reinforcement Learning. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings . OpenReview.net
Barret Zoph and Quoc V. Le. 2017 · 2017
Earlier work this paper cites.
Identifying implementation bugs in machine learning based image classifiers using metamorphic testing. In Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis, ISSTA 2018, Amsterdam, The Netherlands, July 16-21, 2018 , Frank Tip and Eric Bodden (Eds.). ACM, 118–128
Anurag Dwarakanath, Manish Ahuja, Samarth Sikand, Raghotham M. Rao, R. P. Jagadeesh Chandra Bose, Neville Dubash, and Sanjay Podder. 2018 · 2018
Earlier work this paper cites.
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers
Fred Hohman, Minsuk Kahng, Robert Pienta, and Duen Horng Chau. 2019 · 2018
Earlier work this paper cites.
MODE: automated neural network model debugging via state differential analysis and input selection. In Proceedings of the 2018 ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/SIGSOFT FSE 2018, Lake Buena Vista, FL, USA, November 04-09, 2018 , Gary T. Leavens, Alessandro Garcia, and Corina S. Pasareanu (Eds.). ACM, 175–186
Shiqing Ma, Yingqi Liu, Wen-Chuan Lee, Xiangyu Zhang, and Ananth Grama. 2018 · 2018
Earlier work this paper cites.
Multiple-Implementation Testing of Supervised Learning Software. In The Workshops of the The Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA, February 2-7, 2018 (AAAI Workshops, Vol. WS-18) . AAAI Press, 384–391
Siwakorn Srisakaokul, Zhengkai Wu, Angello Astorga, Oreoluwa Alebiosu, and Tao Xie. 2018 · 2018
Earlier work this paper cites.
Seq2seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models
Hendrik Strobelt, Sebastian Gehrmann, Michael Behrisch, Adam Perer, Hanspeter Pfister, and Alexander M. Rush. 2019 · 2018
Earlier work this paper cites.
Taking Human out of Learning Applications: A Survey on Automated Machine Learning
Quanming Yao, Mengshuo Wang, Hugo Jair Escalante, Isabelle Guyon, Yi-Qi Hu, Yu-Feng Li, Wei-Wei Tu, Qiang Yang, and Yang Yu. 2018 · 2018
Cited alongside, same era.
An empirical study on TensorFlow program bugs. In Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis, ISSTA 2018, Amsterdam, The Netherlands, July 16-21, 2018 , Frank Tip and Eric Bodden (Eds.). ACM, 129–140
Yuhao Zhang, Yifan Chen, Shing-Chi Cheung, Yingfei Xiong, and Lu Zhang. 2018 · 2018
Cited alongside, same era.
Neural Architecture Search: A Survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter. 2019 · 2019
Cited alongside, same era.
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA (Proceedings of Machine Learning Research, Vol. 97) , Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 4901–4911
Augustus Odena, Catherine Olsson, David G. Andersen, and Ian J. Goodfellow. 2019 · 2019
UMLAUT: Debugging Deep Learning Programs using Program Structure and Model Behavior. In CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021 , Yoshifumi Kitamura, Aaron Quigley, Katherine Isbister, Takeo Igarashi, Pernille Bjørn, and Steven Mark Drucker (Eds.). ACM, 310:1–310:16
Eldon Schoop, Forrest Huang, and Bjoern Hartmann. 2021 · 2021
Later among the works it cites.
Provable repair of deep neural networks. In PLDI ’21: 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation, Virtual Event, Canada, June 20-25, 2021 , Stephen N. Freund and Eran Yahav (Eds.). ACM, 588–603
Matthew Sotoudeh and Aditya V. Thakur. 2021 · 2021
Later among the works it cites.
Eike Stein, Steffen Herbold, Fabian Trautsch, and Jens Grabowski. 2021 · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
CRADLE: cross-backend validation to detect and localize bugs in deep learning libraries. In Proceedings of the 41st International Conference on Software Engineering, ICSE 2019, Montreal, QC, Canada, May 25-31, 2019 , Joanne M. Atlee, Tevfik Bultan, and Jon Whittle (Eds.). IEEE / ACM, 1027–1038
Hung Viet Pham, Thibaud Lutellier, Weizhen Qi, and Lin Tan. 2019 · 2019
Cited alongside, same era.
A Survey on Deep Learning: Algorithms, Techniques, and Applications
Samira Pouyanfar, Saad Sadiq, Yilin Yan, Haiman Tian, Yudong Tao, Maria E. Presa Reyes, Mei-Ling Shyu, Shu-Ching Chen, and S. S. Iyengar. 2019 · 2019
Cited alongside, same era.
Testing Machine Learning Algorithms for Balanced Data Usage. In 12th IEEE Conference on Software Testing, Validation and Verification, ICST 2019, Xi’an, China, April 22-27, 2019 . IEEE, 125–135
Arnab Sharma and Heike Wehrheim. 2019 · 2019
Cited alongside, same era.
Array programming with NumPy
Charles R. Harris, K. Jarrod Millman, Stéfan van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández del Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant. 2020 · 2020
Cited alongside, same era.
AutoML: A survey of the state-of-the-art
Xin He, Kaiyong Zhao, and Xiaowen Chu. 2021 · 2020
Cited alongside, same era.
Taxonomy of real faults in deep learning systems. In ICSE ’20: 42nd International Conference on Software Engineering, Seoul, South Korea, 27 June - 19 July, 2020 , Gregg Rothermel and Doo-Hwan Bae (Eds.). ACM, 1110–1121
Nargiz Humbatova, Gunel Jahangirova, Gabriele Bavota, Vincenzo Riccio, Andrea Stocco, and Paolo Tonella. 2020 · 2020
Cited alongside, same era.
Repairing deep neural networks: fix patterns and challenges. In ICSE ’20: 42nd International Conference on Software Engineering, Seoul, South Korea, 27 June - 19 July, 2020 , Gregg Rothermel and Doo-Hwan Bae (Eds.). ACM, 1135–1146
Md Johirul Islam, Rangeet Pan, Giang Nguyen, and Hridesh Rajan. 2020 · 2020
Cited alongside, same era.
Documentation-Guided Fuzzing for Testing Deep Learning API Functions
Yitong Li. 2020 · 2020
Cited alongside, same era.
NNrepair: Constraint-Based Repair of Neural Network Classifiers. In Computer Aided Verification - 33rd International Conference, CAV 2021, Virtual Event, July 20-23, 2021, Proceedings, Part I (Lecture Notes in Computer Science, Vol. 12759) , Alexandra Silva and K. Rustan M. Leino (Eds.). Springer, 3–25
Muhammad Usman, Divya Gopinath, Youcheng Sun, Yannic Noller, and Corina S. Pasareanu. 2021 · 2021
Later among the works it cites.
Tensfa: Detecting and Repairing Tensor Shape Faults in Deep Learning Systems. In 2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE) . IEEE, 11–21
Dangwei Wu, Beijun Shen, Yuting Chen, He Jiang, and Lei Qiao. 2021 · 2021
Later among the works it cites.
RNNRepair: Automatic RNN Repair via Model-based Analysis. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event (Proceedings of Machine Learning Research, Vol. 139) , Marina Meila and Tong Zhang (Eds.). PMLR, 11383–11392
Xiaofei Xie, Wenbo Guo, Lei Ma, Wei Le, Jian Wang, Lingjun Zhou, Yang Liu, and Xinyu Xing. 2021 · 2021
Later among the works it cites.
AUTOTRAINER: An Automatic DNN Training Problem Detection and Repair System. In 43rd IEEE/ACM International Conference on Software Engineering, ICSE 2021, Madrid, Spain, 22-30 May 2021 . IEEE, 359–371
Xiaoyu Zhang, Juan Zhai, Shiqing Ma, and Chao Shen. 2021 · 2021
Later among the works it cites.
Fashion Classification Using CNN
2022 · 2022
Later among the works it cites.
MNIST Adversarial Examples Challenge
2022 · 2022
Later among the works it cites.
SVHN Deep Neural Network Image Classification
Laxmi Chaudhary. 2022 · 2022
Later among the works it cites.
ExAIS: Executable AI Semantics. In 44th IEEE/ACM 44th International Conference on Software Engineering, ICSE 2022, Pittsburgh, PA, USA, May 25-27, 2022 . ACM, 859–870
Richard Schumi and Jun Sun. 2022 · 2022
Later among the works it cites.
Causality-based Neural Network Repair. In 44th International Conference on Software Engineering (ICSE 2022) . IEEE
Bing Sun, Jun Sun, Hong Long Pham, and Jie Shi. 2022 · 2022
Later among the works it cites.
DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs. In 44th IEEE/ACM 44th International Conference on Software Engineering, ICSE 2022, Pittsburgh, PA, USA, May 25-27, 2022 . ACM, 561–572
Mohammad Wardat, Breno Dantas Cruz, Wei Le, and Hridesh Rajan. 2022 · 2022
Later among the works it cites.
Automatically repairing tensor shape faults in deep learning programs
Dangwei Wu, Beijun Shen, Yuting Chen, He Jiang, and Lei Qiao. 2022 · 2022
Later among the works it cites.
Neural Network Repair with Reachability Analysis. In Formal Modeling and Analysis of Timed Systems - 20th International Conference, FORMATS 2022, Warsaw, Poland, September 13-15, 2022, Proceedings (Lecture Notes in Computer Science, Vol. 13465) , Sergiy Bogomolov and David Parker (Eds.). Springer, 221–236
Xiaodong Yang, Tom Yamaguchi, Hoang-Dung Tran, Bardh Hoxha, Taylor T. Johnson, and Danil V. Prokhorov. 2022 · 2022
Later among the works it cites.
ExAIS: Executable AI Semantics Repository with AI Framework Testing and AI Model Repair Tools
2023 · 2023
Closest in time.