TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
暂无分享,去创建一个
[1] David Goldberg. What Every Computer Scientist Should Know About Floating-Point Arithmetic , 1992 .
[2] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[3] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[4] Piotr Indyk,et al. Approximate nearest neighbors: towards removing the curse of dimensionality , 1998, STOC '98.
[5] K. Claessen,et al. QuickCheck: a lightweight tool for random testing of Haskell programs , 2000, ICFP '00.
[6] J Hayhurst Kelly,et al. A Practical Tutorial on Modified Condition/Decision Coverage , 2001 .
[7] Simon Haykin,et al. GradientBased Learning Applied to Document Recognition , 2001 .
[8] Trevor Darrell,et al. Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing) , 2006 .
[9] Koushik Sen,et al. CUTE: a concolic unit testing engine for C , 2005, ESEC/FSE-13.
[10] Mark John Somers,et al. Neural Networks in Organizational Research: Applying Pattern Recognition to the Analysis of Organizational Behavior , 2006 .
[11] Alexandr Andoni,et al. Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions , 2006, 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06).
[12] Michael Mitzenmacher,et al. Distance-Sensitive Bloom Filters , 2006, ALENEX.
[13] Pierluigi Siano,et al. Real Time Operation of Smart Grids via FCN Networks and Optimal Power Flow , 2012, IEEE Transactions on Industrial Informatics.
[14] David G. Lowe,et al. Scalable Nearest Neighbor Algorithms for High Dimensional Data , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[15] Vladimir Krylov,et al. Approximate nearest neighbor algorithm based on navigable small world graphs , 2014, Inf. Syst..
[16] Alex Graves,et al. Neural Turing Machines , 2014, ArXiv.
[17] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[18] Fernando A. Mujica,et al. An Empirical Evaluation of Deep Learning on Highway Driving , 2015, ArXiv.
[19] Fei-Fei Li,et al. Visualizing and Understanding Recurrent Networks , 2015, ArXiv.
[20] Alexandr Andoni,et al. Practical and Optimal LSH for Angular Distance , 2015, NIPS.
[21] Anelia Angelova,et al. Real-Time Pedestrian Detection with Deep Network Cascades , 2015, BMVC.
[22] Alexandr Andoni,et al. Optimal Data-Dependent Hashing for Approximate Near Neighbors , 2015, STOC.
[23] Yoshua Bengio,et al. Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.
[24] Xin Zhang,et al. End to End Learning for Self-Driving Cars , 2016, ArXiv.
[25] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Subhashini Venugopalan,et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.
[27] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[28] Augustus Odena,et al. Faster Asynchronous SGD , 2016, ArXiv.
[29] Alex Graves,et al. Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes , 2016, NIPS.
[30] David A. Patterson,et al. In-datacenter performance analysis of a tensor processing unit , 2017, 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA).
[31] Junfeng Yang,et al. DeepXplore: Automated Whitebox Testing of Deep Learning Systems , 2017, SOSP.
[32] Abhik Roychoudhury,et al. Directed Greybox Fuzzing , 2017, CCS.
[33] Tao Zhang,et al. A Survey of Model Compression and Acceleration for Deep Neural Networks , 2017, ArXiv.
[34] Jascha Sohl-Dickstein,et al. SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability , 2017, NIPS.
[35] Mykel J. Kochenderfer,et al. Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks , 2017, CAV.
[36] Ran El-Yaniv,et al. Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations , 2016, J. Mach. Learn. Res..
[37] Colin Raffel,et al. Realistic Evaluation of Deep Semi-Supervised Learning Algorithms , 2018, NeurIPS.
[38] Philip Bachman,et al. Deep Reinforcement Learning that Matters , 2017, AAAI.
[39] Andrew M. Dai,et al. Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step , 2017, ICLR.
[40] Lei Ma,et al. DeepGauge: Comprehensive and Multi-Granularity Testing Criteria for Gauging the Robustness of Deep Learning Systems , 2018, ArXiv.
[41] Andrew Ruef,et al. Evaluating Fuzz Testing , 2018, CCS.
[42] M. Kearns,et al. Fairness in Criminal Justice Risk Assessments: The State of the Art , 2017, Sociological Methods & Research.
[43] Mario Lucic,et al. Are GANs Created Equal? A Large-Scale Study , 2017, NeurIPS.
[44] Matthew Wicker,et al. Feature-Guided Black-Box Safety Testing of Deep Neural Networks , 2017, TACAS.
[45] Colin Raffel,et al. Is Generator Conditioning Causally Related to GAN Performance? , 2018, ICML.
[46] Daniel Kroening,et al. Concolic Testing for Deep Neural Networks , 2018, 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE).
[47] Suman Jana,et al. DeepTest: Automated Testing of Deep-Neural-Network-Driven Autonomous Cars , 2017, 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE).
[48] Zachary Chase Lipton. The mythos of model interpretability , 2016, ACM Queue.
[49] Chris Dyer,et al. On the State of the Art of Evaluation in Neural Language Models , 2017, ICLR.
[50] Daniel Kroening,et al. Testing Deep Neural Networks , 2018, ArXiv.
[51] Abhik Roychoudhury,et al. Coverage-Based Greybox Fuzzing as Markov Chain , 2016, IEEE Transactions on Software Engineering.
[52] Alexandr Andoni,et al. Approximate Nearest Neighbor Search in High Dimensions , 2018, Proceedings of the International Congress of Mathematicians (ICM 2018).
[53] Tsong Yueh Chen,et al. Metamorphic Testing: A New Approach for Generating Next Test Cases , 2020, ArXiv.