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Статті в журналах з теми "Fast Gradient Sign Method"
Zou, Junhua, Yexin Duan, Boyu Li, Wu Zhang, Yu Pan, and Zhisong Pan. "Making Adversarial Examples More Transferable and Indistinguishable." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (June 28, 2022): 3662–70. http://dx.doi.org/10.1609/aaai.v36i3.20279.
Повний текст джерелаWibawa, Sigit. "Analysis of Adversarial Attacks on AI-based With Fast Gradient Sign Method." International Journal of Engineering Continuity 2, no. 2 (August 1, 2023): 72–79. http://dx.doi.org/10.58291/ijec.v2i2.120.
Повний текст джерелаSun, Guangling, Yuying Su, Chuan Qin, Wenbo Xu, Xiaofeng Lu, and Andrzej Ceglowski. "Complete Defense Framework to Protect Deep Neural Networks against Adversarial Examples." Mathematical Problems in Engineering 2020 (May 11, 2020): 1–17. http://dx.doi.org/10.1155/2020/8319249.
Повний текст джерелаKim, Hoki, Woojin Lee, and Jaewook Lee. "Understanding Catastrophic Overfitting in Single-step Adversarial Training." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 8119–27. http://dx.doi.org/10.1609/aaai.v35i9.16989.
Повний текст джерелаSaxena, Rishabh, Amit Sanjay Adate, and Don Sasikumar. "A Comparative Study on Adversarial Noise Generation for Single Image Classification." International Journal of Intelligent Information Technologies 16, no. 1 (January 2020): 75–87. http://dx.doi.org/10.4018/ijiit.2020010105.
Повний текст джерелаYang, Bo, Kaiyong Xu, Hengjun Wang, and Hengwei Zhang. "Random Transformation of image brightness for adversarial attack." Journal of Intelligent & Fuzzy Systems 42, no. 3 (February 2, 2022): 1693–704. http://dx.doi.org/10.3233/jifs-211157.
Повний текст джерелаTrinh Quang Kien. "Improving the robustness of binarized neural network using the EFAT method." Journal of Military Science and Technology, CSCE5 (December 15, 2021): 14–23. http://dx.doi.org/10.54939/1859-1043.j.mst.csce5.2021.14-23.
Повний текст джерелаHirano, Hokuto, and Kazuhiro Takemoto. "Simple Iterative Method for Generating Targeted Universal Adversarial Perturbations." Algorithms 13, no. 11 (October 22, 2020): 268. http://dx.doi.org/10.3390/a13110268.
Повний текст джерелаAn, Tong, Tao Zhang, Yanzhang Geng, and Haiquan Jiao. "Normalized Combinations of Proportionate Affine Projection Sign Subband Adaptive Filter." Scientific Programming 2021 (August 26, 2021): 1–12. http://dx.doi.org/10.1155/2021/8826868.
Повний текст джерелаKadhim, Ansam, and Salah Al-Darraji. "Face Recognition System Against Adversarial Attack Using Convolutional Neural Network." Iraqi Journal for Electrical and Electronic Engineering 18, no. 1 (November 6, 2021): 1–8. http://dx.doi.org/10.37917/ijeee.18.1.1.
Повний текст джерелаДисертації з теми "Fast Gradient Sign Method"
Zhang, Zichen. "Local gradient estimate for porous medium and fast diffusion equations by Martingale method." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:551f79f8-b309-4a1f-8afa-c7dc433dad82.
Повний текст джерелаPester, M., and S. Rjasanow. "A parallel version of the preconditioned conjugate gradient method for boundary element equations." Universitätsbibliothek Chemnitz, 1998. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-199800455.
Повний текст джерелаStrauss, Arne Karsten. "Numerical Analysis of Jump-Diffusion Models for Option Pricing." Thesis, Virginia Tech, 2006. http://hdl.handle.net/10919/33917.
Повний текст джерелаMaster of Science
Alli-Oke, Razak Olusegun. "Robustness and optimization in anti-windup control." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/robustness-and-optimization-in-antiwindup-control(8b98c920-90c3-4fbc-95a8-0cc7ae2a607a).html.
Повний текст джерелаVivek, B. S. "Towards Learning Adversarially Robust Deep Learning Models." Thesis, 2019. https://etd.iisc.ac.in/handle/2005/4488.
Повний текст джерелаJuan, Yu-Chin, and 阮毓欽. "A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/32077403329819649481.
Повний текст джерела國立臺灣大學
資訊工程學研究所
102
Matrix factorization is known to be an effective method for recommender systems that are given only the ratings from users to items. Currently, stochastic gradient (SG) method is one of the most popular algorithms for matrix factorization. However, as a sequential approach, SG is difficult to be parallelized for handling web-scale problems. In this thesis, we develop a fast parallel SG method, FPSG, for shared memory systems. By dramatically reducing the cache-miss rate and carefully addressing the load balance of threads, FPSG is more efficient than state-of-the-art parallel algorithms for matrix factorization.
WANG, CHIH-HAO, and 王志豪. "Solving Scattering Problems of Large-Sized Conducting Objects by Conjugate Gradient Algorithm with Fast Multipole Method." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/39689963107809382071.
Повний текст джерела國立海洋大學
電機工程學系
89
In this thesis, we use the method of moment (MoM) to solve the electromagnetic scattering problems. A three-dimension arbitrary-shaped conductive object is divided into triangular patches, and the integral equation is discretized by MoM. Then a conjugate gradient method (CGM) is used to iteratively solve the resulting matrix equation for unknown expansion coefficients for the surface current. But when the number of unknowns is large, the CGM takes more time at each iteration. In view of this, we use the fast multipole method (FMM) to speed up the matrix-vector multiply in the CGM. The FMM reduces the complexity of a matrix-vector multiply from to , where N is the number of unknowns. The program makes use of the object-oriented programming technique and uses visual C++ as a tool to design some practical classes, which are convenient to expand programs further. This FMM algorithm also requires less memory, and hence, large and more practical problems can be solved on a PC computer.
Yu, Zhiru. "A CG-FFT Based Fast Full Wave Imaging Method and its Potential Industrial Applications." Diss., 2015. http://hdl.handle.net/10161/11344.
Повний текст джерелаThis dissertation focuses on a FFT based forward EM solver and its application in inverse problems. The main contributions of this work are two folded. On the one hand, it presents the first scaled lab experiment system in the oil and gas industry for through casing hydraulic fracture evaluation. This system is established to validate the feasibility of contrasts enhanced fractures evaluation. On the other hand, this work proposes a FFT based VIE solver for hydraulic fracture evaluation. This efficient solver is needed for numerical analysis of such problem. The solver is then generalized to accommodate scattering simulations for anisotropic inhomogeneous magnetodielectric objects. The inverse problem on anisotropic objects are also studied.
Before going into details of specific applications, some background knowledge is presented. This dissertation starts with an introduction to inverse problems. Then algorithms for forward and inverse problems are discussed. The discussion on forward problem focuses on the VIE formulation and a frequency domain solver. Discussion on inverse problems focuses on iterative methods.
The rest of the dissertation is organized by the two categories of inverse problems, namely the inverse source problem and the inverse scattering problem.
The inverse source problem is studied via an application in microelectronics. In this application, a FFT based inverse source solver is applied to process near field data obtained by near field scanners. Examples show that, with the help of this inverse source solver, the resolution of unknown current source images on a device under test is greatly improved. Due to the improvement in resolution, more flexibility is given to the near field scan system.
Both the forward and inverse solver for inverse scattering problems are studied in detail. As a forward solver for inverse scattering problems, a fast FFT based method for solving VIE of magnetodielectric objects with large electromagnetic contrasts are presented due to the increasing interest in contrasts enhanced full wave EM imaging. This newly developed VIE solver assigns different basis functions of different orders to expand flux densities and vector potentials. Thus, it is called the mixed ordered BCGS-FFT method. The mixed order BCGS-FFT method maintains benefits of high order basis functions for VIE while keeping correct boundary conditions for flux densities and vector potentials. Examples show that this method has an excellent performance on both isotropic and anisotropic objects with high contrasts. Examples also verify that this method is valid in both high and low frequencies. Based on the mixed order BCGS-FFT method, an inverse scattering solver for anisotropic objects is studied. The inverse solver is formulated and solved by the variational born iterative method. An example given in this section shows a successful inversion on an anisotropic magnetodielectric object.
Finally, a lab scale hydraulic fractures evaluation system for oil/gas reservoir based on previous discussed inverse solver is presented. This system has been setup to verify the numerical results obtained from previously described inverse solvers. These scaled experiments verify the accuracy of the forward solver as well as the performance of the inverse solver. Examples show that the inverse scattering model is able to evaluate contrasts enhanced hydraulic fractures in a shale formation. Furthermore, this system, for the first time in the oil and gas industry, verifies that hydraulic fractures can be imaged through a metallic casing.
Dissertation
Книги з теми "Fast Gradient Sign Method"
Pan, Victor. A fast, preconditioned conjugate gradient Toeplitz solver. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1989.
Знайти повний текст джерелаEvtushenko, Yury, Vladimir Zubov, and Anna Albu. Optimal control of thermal processes with phase transitions. LCC MAKS Press, 2021. http://dx.doi.org/10.29003/m2449.978-5-317-06677-2.
Повний текст джерелаЧастини книг з теми "Fast Gradient Sign Method"
Muncsan, Tamás, and Attila Kiss. "Transferability of Fast Gradient Sign Method." In Advances in Intelligent Systems and Computing, 23–34. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55187-2_3.
Повний текст джерелаXia, Xiaoyan, Wei Xue, Pengcheng Wan, Hui Zhang, Xinyu Wang, and Zhiting Zhang. "FCGSM: Fast Conjugate Gradient Sign Method for Adversarial Attack on Image Classification." In Lecture Notes in Electrical Engineering, 709–16. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2287-1_98.
Повний текст джерелаWang, Jiangqin, and Wen Gao. "A Fast Sign Word Recognition Method for Chinese Sign Language." In Advances in Multimodal Interfaces — ICMI 2000, 599–606. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-40063-x_78.
Повний текст джерелаTian, Zhiyi, Chenhan Zhang, Lei Cui, and Shui Yu. "GSMI: A Gradient Sign Optimization Based Model Inversion Method." In Lecture Notes in Computer Science, 67–78. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97546-3_6.
Повний текст джерелаChen, Cheng, Zhiguang Wang, Yongnian Fan, Xue Zhang, Dawei Li, and Qiang Lu. "Nesterov Adam Iterative Fast Gradient Method for Adversarial Attacks." In Lecture Notes in Computer Science, 586–98. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15919-0_49.
Повний текст джерелаNecoara, I. "Rate Analysis of Inexact Dual Fast Gradient Method for Distributed MPC." In Intelligent Systems, Control and Automation: Science and Engineering, 163–78. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7006-5_10.
Повний текст джерелаChen, Li, Hongzhi Zhang, Dongwei Ren, David Zhang, and Wangmeng Zuo. "Fast Augmented Lagrangian Method for Image Smoothing with Hyper-Laplacian Gradient Prior." In Communications in Computer and Information Science, 12–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45643-9_2.
Повний текст джерелаChernov, Alexey, Pavel Dvurechensky, and Alexander Gasnikov. "Fast Primal-Dual Gradient Method for Strongly Convex Minimization Problems with Linear Constraints." In Discrete Optimization and Operations Research, 391–403. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44914-2_31.
Повний текст джерелаCátedra, M. F., Rafael P. Torres, and Jesús G. Cuevas. "A method to analyze scattering from general periodic screens using Fast Fourier Transform and Conjugate Gradient method." In Electromagnetic Modelling and Measurements for Analysis and Synthesis Problems, 145–60. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3232-9_9.
Повний текст джерелаLin, Yuhui, Zhiyi Qu, Yu Zhang, and Huiyi Han. "A Fast and Accurate Pupil Localization Method Using Gray Gradient Differential and Curve Fitting." In Proceedings of the 4th International Conference on Computer Engineering and Networks, 495–503. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11104-9_58.
Повний текст джерелаТези доповідей конференцій з теми "Fast Gradient Sign Method"
Liu, Yujie, Shuai Mao, Xiang Mei, Tao Yang, and Xuran Zhao. "Sensitivity of Adversarial Perturbation in Fast Gradient Sign Method." In 2019 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2019. http://dx.doi.org/10.1109/ssci44817.2019.9002856.
Повний текст джерелаXu, Jin. "Generate Adversarial Examples by Nesterov-momentum Iterative Fast Gradient Sign Method." In 2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2020. http://dx.doi.org/10.1109/icsess49938.2020.9237700.
Повний текст джерелаHong, In-pyo, Gyu-ho Choi, Pan-koo Kim, and Chang Choi. "Security Verification Software Platform of Data-efficient Image Transformer Based on Fast Gradient Sign Method." In SAC '23: 38th ACM/SIGAPP Symposium on Applied Computing. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3555776.3577731.
Повний текст джерелаHassan, Muhammad, Shahzad Younis, Ahmed Rasheed, and Muhammad Bilal. "Integrating single-shot Fast Gradient Sign Method (FGSM) with classical image processing techniques for generating adversarial attacks on deep learning classifiers." In Fourteenth International Conference on Machine Vision (ICMV 2021), edited by Wolfgang Osten, Dmitry Nikolaev, and Jianhong Zhou. SPIE, 2022. http://dx.doi.org/10.1117/12.2623585.
Повний текст джерелаReyes-Amezcua, Ivan, Gilberto Ochoa-Ruiz, and Andres Mendez-Vazquez. "Transfer Robustness to Downstream Tasks Through Sampling Adversarial Perturbations." In LatinX in AI at Computer Vision and Pattern Recognition Conference 2023. Journal of LatinX in AI Research, 2023. http://dx.doi.org/10.52591/lxai2023061811.
Повний текст джерелаSilva, Gabriel H. N. Espindola da, Rodrigo Sanches Miani, and Bruno Bogaz Zarpelão. "Investigando o Impacto de Amostras Adversárias na Detecção de Intrusões em um Sistema Ciberfísico." In Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/sbrc.2023.488.
Повний текст джерелаMohandas, Sreenivasan, Naresh Manwani, and Durga Dhulipudi. "Momentum Iterative Gradient Sign Method Outperforms PGD Attacks." In 14th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0010938400003116.
Повний текст джерелаChen, Annie I., and Asuman Ozdaglar. "A fast distributed proximal-gradient method." In 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2012. http://dx.doi.org/10.1109/allerton.2012.6483273.
Повний текст джерелаMineo, Taiyo, and Hayaru Shouno. "Improving Convergence Rate of Sign Algorithm using Natural Gradient Method." In 2021 29th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco54536.2021.9616060.
Повний текст джерелаSujee, R., and S. Padmavathi. "Fast Texture Classification using Gradient Histogram Method." In 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2020. http://dx.doi.org/10.1109/icaccs48705.2020.9074355.
Повний текст джерелаЗвіти організацій з теми "Fast Gradient Sign Method"
Peter W. Carr, K.M. Fuller, D.R. Stoll, L.D. Steinkraus, M.S. Pasha, and Glenn G. Hardin. Fast Gradient Elution Reversed-Phase HPLC with Diode-Array Detection as a High Throughput Screening Method for Drugs of Abuse. Office of Scientific and Technical Information (OSTI), December 2005. http://dx.doi.org/10.2172/892807.
Повний текст джерела