Artigos de revistas sobre o tema "Adaptive gradient methods with momentum"
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Abdulkadirov, R. I., e P. A. Lyakhov. "A new approach to training neural networks using natural gradient descent with momentum based on Dirichlet distributions". Computer Optics 47, n.º 1 (fevereiro de 2023): 160–69. http://dx.doi.org/10.18287/2412-6179-co-1147.
Texto completo da fonteREHMAN, MUHAMMAD ZUBAIR, e NAZRI MOHD NAWI. "STUDYING THE EFFECT OF ADAPTIVE MOMENTUM IN IMPROVING THE ACCURACY OF GRADIENT DESCENT BACK PROPAGATION ALGORITHM ON CLASSIFICATION PROBLEMS". International Journal of Modern Physics: Conference Series 09 (janeiro de 2012): 432–39. http://dx.doi.org/10.1142/s201019451200551x.
Texto completo da fonteChen, Ruijuan, Xiaoquan Tang e Xiuting Li. "Adaptive Stochastic Gradient Descent Method for Convex and Non-Convex Optimization". Fractal and Fractional 6, n.º 12 (29 de novembro de 2022): 709. http://dx.doi.org/10.3390/fractalfract6120709.
Texto completo da fonteZhang, Yue, Seong-Yoon Shin, Xujie Tan e Bin Xiong. "A Self-Adaptive Approximated-Gradient-Simulation Method for Black-Box Adversarial Sample Generation". Applied Sciences 13, n.º 3 (18 de janeiro de 2023): 1298. http://dx.doi.org/10.3390/app13031298.
Texto completo da fonteLong, Sheng, Wei Tao, Shuohao LI, Jun Lei e Jun Zhang. "On the Convergence of an Adaptive Momentum Method for Adversarial Attacks". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 13 (24 de março de 2024): 14132–40. http://dx.doi.org/10.1609/aaai.v38i13.29323.
Texto completo da fonteZhang, Jiahui, Xinhao Yang, Ke Zhang e Chenrui Wen. "An Adaptive Deep Learning Optimization Method Based on Radius of Curvature". Computational Intelligence and Neuroscience 2021 (10 de novembro de 2021): 1–10. http://dx.doi.org/10.1155/2021/9882068.
Texto completo da fonteZang, Yu, Zhe Xue, Shilong Ou, Lingyang Chu, Junping Du e Yunfei Long. "Efficient Asynchronous Federated Learning with Prospective Momentum Aggregation and Fine-Grained Correction". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 15 (24 de março de 2024): 16642–50. http://dx.doi.org/10.1609/aaai.v38i15.29603.
Texto completo da fonteLiu, Miaomiao, Dan Yao, Zhigang Liu, Jingfeng Guo e Jing Chen. "An Improved Adam Optimization Algorithm Combining Adaptive Coefficients and Composite Gradients Based on Randomized Block Coordinate Descent". Computational Intelligence and Neuroscience 2023 (10 de janeiro de 2023): 1–14. http://dx.doi.org/10.1155/2023/4765891.
Texto completo da fonteJiang, Shuoran, Qingcai Chen, Youcheng Pan, Yang Xiang, Yukang Lin, Xiangping Wu, Chuanyi Liu e Xiaobao Song. "ZO-AdaMU Optimizer: Adapting Perturbation by the Momentum and Uncertainty in Zeroth-Order Optimization". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 16 (24 de março de 2024): 18363–71. http://dx.doi.org/10.1609/aaai.v38i16.29796.
Texto completo da fonteSineglazov, Victor, e Anatoly Kot. "Design of hybrid neural networks of the ensemble structure". Eastern-European Journal of Enterprise Technologies 1, n.º 4 (109) (26 de fevereiro de 2021): 31–45. http://dx.doi.org/10.15587/1729-4061.2021.225301.
Texto completo da fonteZhang, Jack, Guan Xiong Qiao, Alexandru Lopotenco e Ian Tong Pan. "Understanding Stochastic Optimization Behavior at the Layer Update Level (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 11 (28 de junho de 2022): 13109–10. http://dx.doi.org/10.1609/aaai.v36i11.21691.
Texto completo da fonteZhang, Qikun, Yuzhi Zhang, Yanling Shao, Mengqi Liu, Jianyong Li, Junling Yuan e Ruifang Wang. "Boosting Adversarial Attacks with Nadam Optimizer". Electronics 12, n.º 6 (20 de março de 2023): 1464. http://dx.doi.org/10.3390/electronics12061464.
Texto completo da fonteYi, Dokkyun, Sangmin Ji e Sunyoung Bu. "An Enhanced Optimization Scheme Based on Gradient Descent Methods for Machine Learning". Symmetry 11, n.º 7 (20 de julho de 2019): 942. http://dx.doi.org/10.3390/sym11070942.
Texto completo da fonteSun, Yunyun, Yutong Liu, Haocheng Zhou e Huijuan Hu. "Plant Diseases Identification through a Discount Momentum Optimizer in Deep Learning". Applied Sciences 11, n.º 20 (12 de outubro de 2021): 9468. http://dx.doi.org/10.3390/app11209468.
Texto completo da fonteKoudounas, Alkis, e Simone Fiori. "Gradient-based Learning Methods Extended to Smooth Manifolds Applied to Automated Clustering". Journal of Artificial Intelligence Research 68 (17 de agosto de 2020): 777–816. http://dx.doi.org/10.1613/jair.1.12192.
Texto completo da fonteTchórzewski, Jerzy, e Tomasz Mielcarz. "Selection of an algorithm for classifying data quoted on the Day Ahead Market of TGE S.A. in MATLAB and Simulink using Deep Learning Toolbox". Studia Informatica. System and information technology 28, n.º 1 (1 de dezembro de 2023): 83–108. http://dx.doi.org/10.34739/si.2023.28.05.
Texto completo da fonteSong, Ci. "The performance analysis of Adam and SGD in image classification and generation tasks". Applied and Computational Engineering 5, n.º 1 (14 de junho de 2023): 757–63. http://dx.doi.org/10.54254/2755-2721/5/20230697.
Texto completo da fonteSen, Alper, e Kutalmis Gumus. "Comparison of Different Parameters of Feedforward Backpropagation Neural Networks in DEM Height Estimation for Different Terrain Types and Point Distributions". Systems 11, n.º 5 (19 de maio de 2023): 261. http://dx.doi.org/10.3390/systems11050261.
Texto completo da fonteJin, Yong, Yiwen Yang, Baican Yang e Yunfu Zhang. "An Adaptive BP Neural Network Model for Teaching Quality Evaluation in Colleges and Universities". Wireless Communications and Mobile Computing 2021 (10 de agosto de 2021): 1–7. http://dx.doi.org/10.1155/2021/4936873.
Texto completo da fonteHan, Bao Ru, Jing Bing Li e Heng Yu Wu. "Tolerance Analog Circuit Hard Fault and Soft Fault Diagnosis Based on Particle Swarm Neural Network". Advanced Materials Research 712-715 (junho de 2013): 1965–69. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.1965.
Texto completo da fonteAn, Feng-Ping, Jun-e. Liu e Lei Bai. "Pedestrian Reidentification Algorithm Based on Deconvolution Network Feature Extraction-Multilayer Attention Mechanism Convolutional Neural Network". Journal of Sensors 2021 (7 de janeiro de 2021): 1–12. http://dx.doi.org/10.1155/2021/9463092.
Texto completo da fonteZhang, Lin, Yian Zhu, Xianchen Shi e Xuesi Li. "A Situation Assessment Method with an Improved Fuzzy Deep Neural Network for Multiple UAVs". Information 11, n.º 4 (4 de abril de 2020): 194. http://dx.doi.org/10.3390/info11040194.
Texto completo da fonteWu, Xue-Ting, Jun-Ning Liu, Adel Alowaisy, Noriyuki Yasufuku, Ryohei Ishikura e Meilani Adriyati. "Settlement Forecast of Marine Soft Soil Ground Improved with Prefabricated Vertical Drain-Assisted Staged Riprap Filling". Buildings 14, n.º 5 (7 de maio de 2024): 1316. http://dx.doi.org/10.3390/buildings14051316.
Texto completo da fonteÖZALTIN, Öznur, e Özgür YENİAY. "Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture". Communications Faculty Of Science University of Ankara Series A1Mathematics and Statistics 72, n.º 2 (23 de junho de 2023): 482–99. http://dx.doi.org/10.31801/cfsuasmas.1202806.
Texto completo da fonteGao, Yiping. "News Video Classification Model Based on ResNet-2 and Transfer Learning". Security and Communication Networks 2021 (16 de dezembro de 2021): 1–9. http://dx.doi.org/10.1155/2021/5865200.
Texto completo da fonteLi, Yanan, Xuebin Ren, Fangyuan Zhao e Shusen Yang. "A Zeroth-Order Adaptive Learning Rate Method to Reduce Cost of Hyperparameter Tuning for Deep Learning". Applied Sciences 11, n.º 21 (30 de outubro de 2021): 10184. http://dx.doi.org/10.3390/app112110184.
Texto completo da fonteKim, Kyung-Soo, e Yong-Suk Choi. "HyAdamC: A New Adam-Based Hybrid Optimization Algorithm for Convolution Neural Networks". Sensors 21, n.º 12 (12 de junho de 2021): 4054. http://dx.doi.org/10.3390/s21124054.
Texto completo da fonteLiu, Yiqi, Longhua Yuan, Dong Li, Yan Li e Daoping Huang. "Process Monitoring of Quality-Related Variables in Wastewater Treatment Using Kalman-Elman Neural Network-Based Soft-Sensor Modeling". Water 13, n.º 24 (20 de dezembro de 2021): 3659. http://dx.doi.org/10.3390/w13243659.
Texto completo da fonteLin, Rong-Ho, Benjamin Kofi Kujabi, Chun-Ling Chuang, Ching-Shun Lin e Chun-Jen Chiu. "Application of Deep Learning to Construct Breast Cancer Diagnosis Model". Applied Sciences 12, n.º 4 (13 de fevereiro de 2022): 1957. http://dx.doi.org/10.3390/app12041957.
Texto completo da fontenull, Hailiang Liu, e Xuping Tian. "An Adaptive Gradient Method with Energy and Momentum". Annals of Applied Mathematics 38, n.º 2 (junho de 2022): 183–222. http://dx.doi.org/10.4208/aam.oa-2021-0095.
Texto completo da fonteLiu, Jian-Qiang, Da-Zheng Feng e Wei-Wei Zhang. "Adaptive Improved Natural Gradient Algorithm for Blind Source Separation". Neural Computation 21, n.º 3 (março de 2009): 872–89. http://dx.doi.org/10.1162/neco.2008.07-07-562.
Texto completo da fonteLiu, Guoqi, Zhiheng Zhou, Huiqiang Zhong e Shengli Xie. "Gradient descent with adaptive momentum for active contour models". IET Computer Vision 8, n.º 4 (agosto de 2014): 287–98. http://dx.doi.org/10.1049/iet-cvi.2013.0089.
Texto completo da fonteHAMID, NORHAMREEZA ABDUL, NAZRI MOHD NAWI, ROZAIDA GHAZALI e MOHD NAJIB MOHD SALLEH. "SOLVING LOCAL MINIMA PROBLEM IN BACK PROPAGATION ALGORITHM USING ADAPTIVE GAIN, ADAPTIVE MOMENTUM AND ADAPTIVE LEARNING RATE ON CLASSIFICATION PROBLEMS". International Journal of Modern Physics: Conference Series 09 (janeiro de 2012): 448–55. http://dx.doi.org/10.1142/s2010194512005533.
Texto completo da fonteZhang, Wei Tang, e Shao Gang Huang. "Adaptive Neural Network for Image Edge Detection". Advanced Materials Research 524-527 (maio de 2012): 3792–96. http://dx.doi.org/10.4028/www.scientific.net/amr.524-527.3792.
Texto completo da fonteOU, Shi-Feng, Ying GAO e Xiao-Hui ZHAO. "Stochastic Gradient Based Variable Momentum Factor Algorithm for Adaptive Whitening". Acta Automatica Sinica 38, n.º 8 (2012): 1370. http://dx.doi.org/10.3724/sp.j.1004.2012.01370.
Texto completo da fonteXue, Liqi. "Research on SGD Algorithm Using Momentum Strategy". Applied and Computational Engineering 2, n.º 1 (22 de março de 2023): 141–50. http://dx.doi.org/10.54254/2755-2721/2/20220622.
Texto completo da fonteYaqub, Muhammad, Jinchao Feng, M. Sultan Zia, Kaleem Arshid, Kebin Jia, Zaka Ur Rehman e Atif Mehmood. "State-of-the-Art CNN Optimizer for Brain Tumor Segmentation in Magnetic Resonance Images". Brain Sciences 10, n.º 7 (3 de julho de 2020): 427. http://dx.doi.org/10.3390/brainsci10070427.
Texto completo da fonteLiang, Dong, Fanfan Ma e Wenyan Li. "New Gradient-Weighted Adaptive Gradient Methods With Dynamic Constraints". IEEE Access 8 (2020): 110929–42. http://dx.doi.org/10.1109/access.2020.3002590.
Texto completo da fonteZhou, Bin, Li Gao e Yu-Hong Dai. "Gradient Methods with Adaptive Step-Sizes". Computational Optimization and Applications 35, n.º 1 (31 de março de 2006): 69–86. http://dx.doi.org/10.1007/s10589-006-6446-0.
Texto completo da fonteTseng, Paul. "An Incremental Gradient(-Projection) Method with Momentum Term and Adaptive Stepsize Rule". SIAM Journal on Optimization 8, n.º 2 (maio de 1998): 506–31. http://dx.doi.org/10.1137/s1052623495294797.
Texto completo da fonteShao, Hongmei, Dongpo Xu e Gaofeng Zheng. "Convergence of a Batch Gradient Algorithm with Adaptive Momentum for Neural Networks". Neural Processing Letters 34, n.º 3 (22 de julho de 2011): 221–28. http://dx.doi.org/10.1007/s11063-011-9193-x.
Texto completo da fonteBoffi, Nicholas M., e Jean-Jacques E. Slotine. "Implicit Regularization and Momentum Algorithms in Nonlinearly Parameterized Adaptive Control and Prediction". Neural Computation 33, n.º 3 (março de 2021): 590–673. http://dx.doi.org/10.1162/neco_a_01360.
Texto completo da fonteFang, Qionglin, e X. U. E. Han. "A Nonlinear Gradient Domain-Guided Filter Optimized by Fractional-Order Gradient Descent with Momentum RBF Neural Network for Ship Image Dehazing". Journal of Sensors 2021 (2 de janeiro de 2021): 1–15. http://dx.doi.org/10.1155/2021/8864906.
Texto completo da fonteArthur, C. K., V. A. Temeng e Y. Y. Ziggah. "Performance Evaluation of Training Algorithms in Backpropagation Neural Network Approach to Blast-Induced Ground Vibration Prediction". Ghana Mining Journal 20, n.º 1 (7 de julho de 2020): 20–33. http://dx.doi.org/10.4314/gm.v20i1.3.
Texto completo da fonteWanto, Anjar. "Prediksi Angka Partisipasi Sekolah dengan Fungsi Pelatihan Gradient Descent With Momentum & Adaptive LR". ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA 3, n.º 1 (30 de abril de 2019): 9. http://dx.doi.org/10.30829/algoritma.v3i1.4431.
Texto completo da fonteYang, Yang, Lipo Mo, Yusen Hu e Fei Long. "The Improved Stochastic Fractional Order Gradient Descent Algorithm". Fractal and Fractional 7, n.º 8 (18 de agosto de 2023): 631. http://dx.doi.org/10.3390/fractalfract7080631.
Texto completo da fonteFrassoldati, Giacomo, Luca Zanni e Gaetano Zanghirati. "New adaptive stepsize selections in gradient methods". Journal of Industrial & Management Optimization 4, n.º 2 (2008): 299–312. http://dx.doi.org/10.3934/jimo.2008.4.299.
Texto completo da fonteGong, Xiaolin, e Xiaoshuang Ding. "Adaptive CDKF Based on Gradient Descent With Momentum and its Application to POS". IEEE Sensors Journal 21, n.º 14 (15 de julho de 2021): 16201–12. http://dx.doi.org/10.1109/jsen.2021.3076071.
Texto completo da fonteShao, Hongmei, Dongpo Xu, Gaofeng Zheng e Lijun Liu. "Convergence of an online gradient method with inner-product penalty and adaptive momentum". Neurocomputing 77, n.º 1 (fevereiro de 2012): 243–52. http://dx.doi.org/10.1016/j.neucom.2011.09.003.
Texto completo da fonteHan, Xiaohui, e Jianping Dong. "Applications of fractional gradient descent method with adaptive momentum in BP neural networks". Applied Mathematics and Computation 448 (julho de 2023): 127944. http://dx.doi.org/10.1016/j.amc.2023.127944.
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