PyTorchFI: A Runtime Perturbation Tool for DNNs
暂无分享,去创建一个
Sarita V. Adve | Abdulrahman Mahmoud | Neeraj Aggarwal | Christopher W. Fletcher | Siva Kumar Sastry Hari | Iuri Frosio | Jose Rodrigo Sanchez Vicarte | Alex Nobbe | I. Frosio | S. Adve | Abdulrahman Mahmoud | S. Hari | Neeraj Aggarwal | Alex Nobbe
[1] Timothy A. Mann,et al. On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models , 2018, ArXiv.
[2] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[3] Zitao Chen,et al. TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications , 2020, 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE).
[4] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[5] Abhishek Das,et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[6] Guanpeng Li,et al. BinFI: an efficient fault injector for safety-critical machine learning systems , 2019, SC.
[7] David A. Patterson,et al. The Berkeley Out-of-Order Machine (BOOM): An Industry-Competitive, Synthesizable, Parameterized RISC-V Processor , 2015 .
[8] Homa Alemzadeh,et al. Experimental Resilience Assessment of an Open-Source Driving Agent , 2018, 2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing (PRDC).
[9] Karthik Pattabiraman,et al. LLFI: An Intermediate Code-Level Fault Injection Tool for Hardware Faults , 2015, 2015 IEEE International Conference on Software Quality, Reliability and Security.
[10] Andrew Zisserman,et al. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps , 2013, ICLR.
[11] Gu-Yeon Wei,et al. Ares: A framework for quantifying the resilience of deep neural networks , 2018, 2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC).
[12] Blaine Nelson,et al. Exploiting Machine Learning to Subvert Your Spam Filter , 2008, LEET.
[13] Cristina Nita-Rotaru,et al. On the Practicality of Integrity Attacks on Document-Level Sentiment Analysis , 2014, AISec '14.
[14] Guanpeng Li,et al. Evaluating Compiler IR-Level Selective Instruction Duplication with Realistic Hardware Errors , 2019, 2019 IEEE/ACM 9th Workshop on Fault Tolerance for HPC at eXtreme Scale (FTXS).
[15] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[16] Song Han,et al. Deep compression and EIE: Efficient inference engine on compressed deep neural network , 2016, 2016 IEEE Hot Chips 28 Symposium (HCS).
[17] Joel Emer,et al. Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks , 2016, CARN.
[18] Ravishankar K. Iyer,et al. ML-Based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection , 2019, 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN).
[19] Guanpeng Li,et al. Understanding Error Propagation in Deep Learning Neural Network (DNN) Accelerators and Applications , 2017, SC17: International Conference for High Performance Computing, Networking, Storage and Analysis.
[20] Blaine Nelson,et al. Poisoning Attacks against Support Vector Machines , 2012, ICML.
[21] Andrea Vedaldi,et al. Salient Deconvolutional Networks , 2016, ECCV.
[22] Thomas Brox,et al. Striving for Simplicity: The All Convolutional Net , 2014, ICLR.
[23] Charbel Sakr,et al. An Analytical Method to Determine Minimum Per-Layer Precision of Deep Neural Networks , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[24] Ali Farhadi,et al. YOLOv3: An Incremental Improvement , 2018, ArXiv.
[25] Stephen W. Keckler,et al. SASSIFI: An architecture-level fault injection tool for GPU application resilience evaluation , 2017, 2017 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS).
[26] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[27] James Newsome,et al. Paragraph: Thwarting Signature Learning by Training Maliciously , 2006, RAID.
[28] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Claudia Eckert,et al. Is Feature Selection Secure against Training Data Poisoning? , 2015, ICML.
[30] Jason Yosinski,et al. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Bolei Zhou,et al. Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[34] Aleksander Madry,et al. On Evaluating Adversarial Robustness , 2019, ArXiv.
[35] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[36] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[37] Wenke Lee,et al. Misleading worm signature generators using deliberate noise injection , 2006, 2006 IEEE Symposium on Security and Privacy (S&P'06).