Journal articles on the topic 'Adaptive gradient methods with momentum'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Adaptive gradient methods with momentum.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
Abdulkadirov, R. I., and P. A. Lyakhov. "A new approach to training neural networks using natural gradient descent with momentum based on Dirichlet distributions." Computer Optics 47, no. 1 (February 2023): 160–69. http://dx.doi.org/10.18287/2412-6179-co-1147.
REHMAN, MUHAMMAD ZUBAIR, and 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 (January 2012): 432–39. http://dx.doi.org/10.1142/s201019451200551x.
Chen, Ruijuan, Xiaoquan Tang, and Xiuting Li. "Adaptive Stochastic Gradient Descent Method for Convex and Non-Convex Optimization." Fractal and Fractional 6, no. 12 (November 29, 2022): 709. http://dx.doi.org/10.3390/fractalfract6120709.
Zhang, Yue, Seong-Yoon Shin, Xujie Tan, and Bin Xiong. "A Self-Adaptive Approximated-Gradient-Simulation Method for Black-Box Adversarial Sample Generation." Applied Sciences 13, no. 3 (January 18, 2023): 1298. http://dx.doi.org/10.3390/app13031298.
Long, Sheng, Wei Tao, Shuohao LI, Jun Lei, and Jun Zhang. "On the Convergence of an Adaptive Momentum Method for Adversarial Attacks." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (March 24, 2024): 14132–40. http://dx.doi.org/10.1609/aaai.v38i13.29323.
Zhang, Jiahui, Xinhao Yang, Ke Zhang, and Chenrui Wen. "An Adaptive Deep Learning Optimization Method Based on Radius of Curvature." Computational Intelligence and Neuroscience 2021 (November 10, 2021): 1–10. http://dx.doi.org/10.1155/2021/9882068.
Zang, Yu, Zhe Xue, Shilong Ou, Lingyang Chu, Junping Du, and Yunfei Long. "Efficient Asynchronous Federated Learning with Prospective Momentum Aggregation and Fine-Grained Correction." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (March 24, 2024): 16642–50. http://dx.doi.org/10.1609/aaai.v38i15.29603.
Liu, Miaomiao, Dan Yao, Zhigang Liu, Jingfeng Guo, and Jing Chen. "An Improved Adam Optimization Algorithm Combining Adaptive Coefficients and Composite Gradients Based on Randomized Block Coordinate Descent." Computational Intelligence and Neuroscience 2023 (January 10, 2023): 1–14. http://dx.doi.org/10.1155/2023/4765891.
Jiang, Shuoran, Qingcai Chen, Youcheng Pan, Yang Xiang, Yukang Lin, Xiangping Wu, Chuanyi Liu, and 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, no. 16 (March 24, 2024): 18363–71. http://dx.doi.org/10.1609/aaai.v38i16.29796.
Sineglazov, Victor, and Anatoly Kot. "Design of hybrid neural networks of the ensemble structure." Eastern-European Journal of Enterprise Technologies 1, no. 4 (109) (February 26, 2021): 31–45. http://dx.doi.org/10.15587/1729-4061.2021.225301.
Zhang, Jack, Guan Xiong Qiao, Alexandru Lopotenco, and Ian Tong Pan. "Understanding Stochastic Optimization Behavior at the Layer Update Level (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 13109–10. http://dx.doi.org/10.1609/aaai.v36i11.21691.
Zhang, Qikun, Yuzhi Zhang, Yanling Shao, Mengqi Liu, Jianyong Li, Junling Yuan, and Ruifang Wang. "Boosting Adversarial Attacks with Nadam Optimizer." Electronics 12, no. 6 (March 20, 2023): 1464. http://dx.doi.org/10.3390/electronics12061464.
Yi, Dokkyun, Sangmin Ji, and Sunyoung Bu. "An Enhanced Optimization Scheme Based on Gradient Descent Methods for Machine Learning." Symmetry 11, no. 7 (July 20, 2019): 942. http://dx.doi.org/10.3390/sym11070942.
Sun, Yunyun, Yutong Liu, Haocheng Zhou, and Huijuan Hu. "Plant Diseases Identification through a Discount Momentum Optimizer in Deep Learning." Applied Sciences 11, no. 20 (October 12, 2021): 9468. http://dx.doi.org/10.3390/app11209468.
Koudounas, Alkis, and Simone Fiori. "Gradient-based Learning Methods Extended to Smooth Manifolds Applied to Automated Clustering." Journal of Artificial Intelligence Research 68 (August 17, 2020): 777–816. http://dx.doi.org/10.1613/jair.1.12192.
Tchórzewski, Jerzy, and 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, no. 1 (December 1, 2023): 83–108. http://dx.doi.org/10.34739/si.2023.28.05.
Song, Ci. "The performance analysis of Adam and SGD in image classification and generation tasks." Applied and Computational Engineering 5, no. 1 (June 14, 2023): 757–63. http://dx.doi.org/10.54254/2755-2721/5/20230697.
Sen, Alper, and Kutalmis Gumus. "Comparison of Different Parameters of Feedforward Backpropagation Neural Networks in DEM Height Estimation for Different Terrain Types and Point Distributions." Systems 11, no. 5 (May 19, 2023): 261. http://dx.doi.org/10.3390/systems11050261.
Jin, Yong, Yiwen Yang, Baican Yang, and Yunfu Zhang. "An Adaptive BP Neural Network Model for Teaching Quality Evaluation in Colleges and Universities." Wireless Communications and Mobile Computing 2021 (August 10, 2021): 1–7. http://dx.doi.org/10.1155/2021/4936873.
Han, Bao Ru, Jing Bing Li, and Heng Yu Wu. "Tolerance Analog Circuit Hard Fault and Soft Fault Diagnosis Based on Particle Swarm Neural Network." Advanced Materials Research 712-715 (June 2013): 1965–69. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.1965.
An, Feng-Ping, Jun-e. Liu, and Lei Bai. "Pedestrian Reidentification Algorithm Based on Deconvolution Network Feature Extraction-Multilayer Attention Mechanism Convolutional Neural Network." Journal of Sensors 2021 (January 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/9463092.
Zhang, Lin, Yian Zhu, Xianchen Shi, and Xuesi Li. "A Situation Assessment Method with an Improved Fuzzy Deep Neural Network for Multiple UAVs." Information 11, no. 4 (April 4, 2020): 194. http://dx.doi.org/10.3390/info11040194.
Wu, Xue-Ting, Jun-Ning Liu, Adel Alowaisy, Noriyuki Yasufuku, Ryohei Ishikura, and Meilani Adriyati. "Settlement Forecast of Marine Soft Soil Ground Improved with Prefabricated Vertical Drain-Assisted Staged Riprap Filling." Buildings 14, no. 5 (May 7, 2024): 1316. http://dx.doi.org/10.3390/buildings14051316.
ÖZALTIN, Öznur, and 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, no. 2 (June 23, 2023): 482–99. http://dx.doi.org/10.31801/cfsuasmas.1202806.
Gao, Yiping. "News Video Classification Model Based on ResNet-2 and Transfer Learning." Security and Communication Networks 2021 (December 16, 2021): 1–9. http://dx.doi.org/10.1155/2021/5865200.
Li, Yanan, Xuebin Ren, Fangyuan Zhao, and Shusen Yang. "A Zeroth-Order Adaptive Learning Rate Method to Reduce Cost of Hyperparameter Tuning for Deep Learning." Applied Sciences 11, no. 21 (October 30, 2021): 10184. http://dx.doi.org/10.3390/app112110184.
Kim, Kyung-Soo, and Yong-Suk Choi. "HyAdamC: A New Adam-Based Hybrid Optimization Algorithm for Convolution Neural Networks." Sensors 21, no. 12 (June 12, 2021): 4054. http://dx.doi.org/10.3390/s21124054.
Liu, Yiqi, Longhua Yuan, Dong Li, Yan Li, and Daoping Huang. "Process Monitoring of Quality-Related Variables in Wastewater Treatment Using Kalman-Elman Neural Network-Based Soft-Sensor Modeling." Water 13, no. 24 (December 20, 2021): 3659. http://dx.doi.org/10.3390/w13243659.
Lin, Rong-Ho, Benjamin Kofi Kujabi, Chun-Ling Chuang, Ching-Shun Lin, and Chun-Jen Chiu. "Application of Deep Learning to Construct Breast Cancer Diagnosis Model." Applied Sciences 12, no. 4 (February 13, 2022): 1957. http://dx.doi.org/10.3390/app12041957.
null, Hailiang Liu, and Xuping Tian. "An Adaptive Gradient Method with Energy and Momentum." Annals of Applied Mathematics 38, no. 2 (June 2022): 183–222. http://dx.doi.org/10.4208/aam.oa-2021-0095.
Liu, Jian-Qiang, Da-Zheng Feng, and Wei-Wei Zhang. "Adaptive Improved Natural Gradient Algorithm for Blind Source Separation." Neural Computation 21, no. 3 (March 2009): 872–89. http://dx.doi.org/10.1162/neco.2008.07-07-562.
Liu, Guoqi, Zhiheng Zhou, Huiqiang Zhong, and Shengli Xie. "Gradient descent with adaptive momentum for active contour models." IET Computer Vision 8, no. 4 (August 2014): 287–98. http://dx.doi.org/10.1049/iet-cvi.2013.0089.
HAMID, NORHAMREEZA ABDUL, NAZRI MOHD NAWI, ROZAIDA GHAZALI, and 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 (January 2012): 448–55. http://dx.doi.org/10.1142/s2010194512005533.
Zhang, Wei Tang, and Shao Gang Huang. "Adaptive Neural Network for Image Edge Detection." Advanced Materials Research 524-527 (May 2012): 3792–96. http://dx.doi.org/10.4028/www.scientific.net/amr.524-527.3792.
OU, Shi-Feng, Ying GAO, and Xiao-Hui ZHAO. "Stochastic Gradient Based Variable Momentum Factor Algorithm for Adaptive Whitening." Acta Automatica Sinica 38, no. 8 (2012): 1370. http://dx.doi.org/10.3724/sp.j.1004.2012.01370.
Xue, Liqi. "Research on SGD Algorithm Using Momentum Strategy." Applied and Computational Engineering 2, no. 1 (March 22, 2023): 141–50. http://dx.doi.org/10.54254/2755-2721/2/20220622.
Yaqub, Muhammad, Jinchao Feng, M. Sultan Zia, Kaleem Arshid, Kebin Jia, Zaka Ur Rehman, and Atif Mehmood. "State-of-the-Art CNN Optimizer for Brain Tumor Segmentation in Magnetic Resonance Images." Brain Sciences 10, no. 7 (July 3, 2020): 427. http://dx.doi.org/10.3390/brainsci10070427.
Liang, Dong, Fanfan Ma, and 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.
Zhou, Bin, Li Gao, and Yu-Hong Dai. "Gradient Methods with Adaptive Step-Sizes." Computational Optimization and Applications 35, no. 1 (March 31, 2006): 69–86. http://dx.doi.org/10.1007/s10589-006-6446-0.
Tseng, Paul. "An Incremental Gradient(-Projection) Method with Momentum Term and Adaptive Stepsize Rule." SIAM Journal on Optimization 8, no. 2 (May 1998): 506–31. http://dx.doi.org/10.1137/s1052623495294797.
Shao, Hongmei, Dongpo Xu, and Gaofeng Zheng. "Convergence of a Batch Gradient Algorithm with Adaptive Momentum for Neural Networks." Neural Processing Letters 34, no. 3 (July 22, 2011): 221–28. http://dx.doi.org/10.1007/s11063-011-9193-x.
Boffi, Nicholas M., and Jean-Jacques E. Slotine. "Implicit Regularization and Momentum Algorithms in Nonlinearly Parameterized Adaptive Control and Prediction." Neural Computation 33, no. 3 (March 2021): 590–673. http://dx.doi.org/10.1162/neco_a_01360.
Fang, Qionglin, and 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 (January 2, 2021): 1–15. http://dx.doi.org/10.1155/2021/8864906.
Arthur, C. K., V. A. Temeng, and Y. Y. Ziggah. "Performance Evaluation of Training Algorithms in Backpropagation Neural Network Approach to Blast-Induced Ground Vibration Prediction." Ghana Mining Journal 20, no. 1 (July 7, 2020): 20–33. http://dx.doi.org/10.4314/gm.v20i1.3.
Wanto, Anjar. "Prediksi Angka Partisipasi Sekolah dengan Fungsi Pelatihan Gradient Descent With Momentum & Adaptive LR." ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA 3, no. 1 (April 30, 2019): 9. http://dx.doi.org/10.30829/algoritma.v3i1.4431.
Yang, Yang, Lipo Mo, Yusen Hu, and Fei Long. "The Improved Stochastic Fractional Order Gradient Descent Algorithm." Fractal and Fractional 7, no. 8 (August 18, 2023): 631. http://dx.doi.org/10.3390/fractalfract7080631.
Frassoldati, Giacomo, Luca Zanni, and Gaetano Zanghirati. "New adaptive stepsize selections in gradient methods." Journal of Industrial & Management Optimization 4, no. 2 (2008): 299–312. http://dx.doi.org/10.3934/jimo.2008.4.299.
Gong, Xiaolin, and Xiaoshuang Ding. "Adaptive CDKF Based on Gradient Descent With Momentum and its Application to POS." IEEE Sensors Journal 21, no. 14 (July 15, 2021): 16201–12. http://dx.doi.org/10.1109/jsen.2021.3076071.
Shao, Hongmei, Dongpo Xu, Gaofeng Zheng, and Lijun Liu. "Convergence of an online gradient method with inner-product penalty and adaptive momentum." Neurocomputing 77, no. 1 (February 2012): 243–52. http://dx.doi.org/10.1016/j.neucom.2011.09.003.
Han, Xiaohui, and Jianping Dong. "Applications of fractional gradient descent method with adaptive momentum in BP neural networks." Applied Mathematics and Computation 448 (July 2023): 127944. http://dx.doi.org/10.1016/j.amc.2023.127944.