Artigos de revistas sobre o tema "Unsupervised deep neural networks"
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Banzi, Jamal, Isack Bulugu e Zhongfu Ye. "Deep Predictive Neural Network: Unsupervised Learning for Hand Pose Estimation". International Journal of Machine Learning and Computing 9, n.º 4 (agosto de 2019): 432–39. http://dx.doi.org/10.18178/ijmlc.2019.9.4.822.
Texto completo da fonteGuo, Wenqi, Weixiong Zhang, Zheng Zhang, Ping Tang e Shichen Gao. "Deep Temporal Iterative Clustering for Satellite Image Time Series Land Cover Analysis". Remote Sensing 14, n.º 15 (29 de julho de 2022): 3635. http://dx.doi.org/10.3390/rs14153635.
Texto completo da fonteXu, Jianqiao, Zhaolu Zuo, Danchao Wu, Bing Li, Xiaoni Li e Deyi Kong. "Bearing Defect Detection with Unsupervised Neural Networks". Shock and Vibration 2021 (19 de agosto de 2021): 1–11. http://dx.doi.org/10.1155/2021/9544809.
Texto completo da fonteFeng, Yu, e Hui Sun. "Basketball Footwork and Application Supported by Deep Learning Unsupervised Transfer Method". International Journal of Information Technology and Web Engineering 18, n.º 1 (1 de dezembro de 2023): 1–17. http://dx.doi.org/10.4018/ijitwe.334365.
Texto completo da fonteSun, Yanan, Gary G. Yen e Zhang Yi. "Evolving Unsupervised Deep Neural Networks for Learning Meaningful Representations". IEEE Transactions on Evolutionary Computation 23, n.º 1 (fevereiro de 2019): 89–103. http://dx.doi.org/10.1109/tevc.2018.2808689.
Texto completo da fonteShi, Yu, Cien Fan, Lian Zou, Caixia Sun e Yifeng Liu. "Unsupervised Adversarial Defense through Tandem Deep Image Priors". Electronics 9, n.º 11 (19 de novembro de 2020): 1957. http://dx.doi.org/10.3390/electronics9111957.
Texto completo da fonteThakur, Amey. "Generative Adversarial Networks". International Journal for Research in Applied Science and Engineering Technology 9, n.º 8 (31 de agosto de 2021): 2307–25. http://dx.doi.org/10.22214/ijraset.2021.37723.
Texto completo da fonteFerles, Christos, Yannis Papanikolaou, Stylianos P. Savaidis e Stelios A. Mitilineos. "Deep Self-Organizing Map of Convolutional Layers for Clustering and Visualizing Image Data". Machine Learning and Knowledge Extraction 3, n.º 4 (14 de novembro de 2021): 879–99. http://dx.doi.org/10.3390/make3040044.
Texto completo da fonteZhuang, Chengxu, Siming Yan, Aran Nayebi, Martin Schrimpf, Michael C. Frank, James J. DiCarlo e Daniel L. K. Yamins. "Unsupervised neural network models of the ventral visual stream". Proceedings of the National Academy of Sciences 118, n.º 3 (11 de janeiro de 2021): e2014196118. http://dx.doi.org/10.1073/pnas.2014196118.
Texto completo da fonteLin, Baihan. "Regularity Normalization: Neuroscience-Inspired Unsupervised Attention across Neural Network Layers". Entropy 24, n.º 1 (28 de dezembro de 2021): 59. http://dx.doi.org/10.3390/e24010059.
Texto completo da fonteAbiyev, Rahib H., e Mohammad Khaleel Sallam Ma’aitah. "Deep Convolutional Neural Networks for Chest Diseases Detection". Journal of Healthcare Engineering 2018 (1 de agosto de 2018): 1–11. http://dx.doi.org/10.1155/2018/4168538.
Texto completo da fonteBrowne, David, Michael Giering e Steven Prestwich. "PulseNetOne: Fast Unsupervised Pruning of Convolutional Neural Networks for Remote Sensing". Remote Sensing 12, n.º 7 (29 de março de 2020): 1092. http://dx.doi.org/10.3390/rs12071092.
Texto completo da fonteYi, Cheng. "Application of Convolutional Networks in Clothing Design from the Perspective of Deep Learning". Scientific Programming 2022 (27 de setembro de 2022): 1–8. http://dx.doi.org/10.1155/2022/6173981.
Texto completo da fonteGhosh, Saheb, Sathis Kumar B e Kathir Deivanai. "DETECTION OF WHALES USING DEEP LEARNING METHODS AND NEURAL NETWORKS". Asian Journal of Pharmaceutical and Clinical Research 10, n.º 13 (1 de abril de 2017): 489. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.20767.
Texto completo da fonteSolomon, Enoch, Abraham Woubie e Krzysztof J. Cios. "UFace: An Unsupervised Deep Learning Face Verification System". Electronics 11, n.º 23 (26 de novembro de 2022): 3909. http://dx.doi.org/10.3390/electronics11233909.
Texto completo da fonteAltuntas, Volkan. "NodeVector: A Novel Network Node Vectorization with Graph Analysis and Deep Learning". Applied Sciences 14, n.º 2 (16 de janeiro de 2024): 775. http://dx.doi.org/10.3390/app14020775.
Texto completo da fonteMa, Chao, Yun Gu, Chen Gong, Jie Yang e Deying Feng. "Unsupervised Video Hashing via Deep Neural Network". Neural Processing Letters 47, n.º 3 (17 de março de 2018): 877–90. http://dx.doi.org/10.1007/s11063-018-9812-x.
Texto completo da fonteNaidu, D. J. Samatha, e T. Mahammad Rafi. "HANDWRITTEN CHARACTER RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS". International Journal of Computer Science and Mobile Computing 10, n.º 8 (30 de agosto de 2021): 41–45. http://dx.doi.org/10.47760/ijcsmc.2021.v10i08.007.
Texto completo da fonteHu, Ruiqi, Shirui Pan, Guodong Long, Qinghua Lu, Liming Zhu e Jing Jiang. "Going Deep: Graph Convolutional Ladder-Shape Networks". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 03 (3 de abril de 2020): 2838–45. http://dx.doi.org/10.1609/aaai.v34i03.5673.
Texto completo da fonteHuang, Qiuyuan, Li Deng, Dapeng Wu, Chang Liu e Xiaodong He. "Attentive Tensor Product Learning". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julho de 2019): 1344–51. http://dx.doi.org/10.1609/aaai.v33i01.33011344.
Texto completo da fonteHuang, Jiabo, Qi Dong, Shaogang Gong e Xiatian Zhu. "Unsupervised Deep Learning via Affinity Diffusion". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 07 (3 de abril de 2020): 11029–36. http://dx.doi.org/10.1609/aaai.v34i07.6757.
Texto completo da fonteTyshchenko, Vitalii. "ANALYSIS OF TRAINING METHODS AND NEURAL NETWORK TOOLS FOR FAKE NEWS DETECTION". Cybersecurity: Education, Science, Technique 4, n.º 20 (2023): 20–34. http://dx.doi.org/10.28925/2663-4023.2023.20.2034.
Texto completo da fonteHeo, Seongmin, e Jay H. Lee. "Statistical Process Monitoring of the Tennessee Eastman Process Using Parallel Autoassociative Neural Networks and a Large Dataset". Processes 7, n.º 7 (1 de julho de 2019): 411. http://dx.doi.org/10.3390/pr7070411.
Texto completo da fonteCao, Yanpeng, Dayan Guan, Weilin Huang, Jiangxin Yang, Yanlong Cao e Yu Qiao. "Pedestrian detection with unsupervised multispectral feature learning using deep neural networks". Information Fusion 46 (março de 2019): 206–17. http://dx.doi.org/10.1016/j.inffus.2018.06.005.
Texto completo da fonteZhang, Pengfei, e Xiaoming Ju. "Adversarial Sample Detection with Gaussian Mixture Conditional Generative Adversarial Networks". Mathematical Problems in Engineering 2021 (13 de setembro de 2021): 1–18. http://dx.doi.org/10.1155/2021/8268249.
Texto completo da fonteKhodayar, Mahdi, e Jacob Regan. "Deep Neural Networks in Power Systems: A Review". Energies 16, n.º 12 (17 de junho de 2023): 4773. http://dx.doi.org/10.3390/en16124773.
Texto completo da fonteLe Roux, Nicolas, e Yoshua Bengio. "Deep Belief Networks Are Compact Universal Approximators". Neural Computation 22, n.º 8 (agosto de 2010): 2192–207. http://dx.doi.org/10.1162/neco.2010.08-09-1081.
Texto completo da fonteZhu, Yi, Xinke Zhou e Xindong Wu. "Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder". Applied Sciences 13, n.º 1 (29 de dezembro de 2022): 481. http://dx.doi.org/10.3390/app13010481.
Texto completo da fonteLin, Yi-Nan, Tsang-Yen Hsieh, Cheng-Ying Yang, Victor RL Shen, Tony Tong-Ying Juang e Wen-Hao Chen. "Deep Petri nets of unsupervised and supervised learning". Measurement and Control 53, n.º 7-8 (9 de junho de 2020): 1267–77. http://dx.doi.org/10.1177/0020294020923375.
Texto completo da fonteSewani, Harshini, e Rasha Kashef. "An Autoencoder-Based Deep Learning Classifier for Efficient Diagnosis of Autism". Children 7, n.º 10 (14 de outubro de 2020): 182. http://dx.doi.org/10.3390/children7100182.
Texto completo da fonteAjay, P., B. Nagaraj, R. Arun Kumar, Ruihang Huang e P. Ananthi. "Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm". Scanning 2022 (6 de junho de 2022): 1–9. http://dx.doi.org/10.1155/2022/1200860.
Texto completo da fonteMamun, Abdullah Al, Em Poh Ping, Jakir Hossen, Anik Tahabilder e Busrat Jahan. "A Comprehensive Review on Lane Marking Detection Using Deep Neural Networks". Sensors 22, n.º 19 (10 de outubro de 2022): 7682. http://dx.doi.org/10.3390/s22197682.
Texto completo da fonteVélez, Paulina, Manuel Miranda, Carmen Serrano e Begoña Acha. "Does a Previous Segmentation Improve the Automatic Detection of Basal Cell Carcinoma Using Deep Neural Networks?" Applied Sciences 12, n.º 4 (17 de fevereiro de 2022): 2092. http://dx.doi.org/10.3390/app12042092.
Texto completo da fonteChu, Lei, Hao Pan e Wenping Wang. "Unsupervised Shape Completion via Deep Prior in the Neural Tangent Kernel Perspective". ACM Transactions on Graphics 40, n.º 3 (4 de julho de 2021): 1–17. http://dx.doi.org/10.1145/3459234.
Texto completo da fonteLi, Yibing, Sitong Zhang, Xiang Li e Fang Ye. "Remote Sensing Image Classification with Few Labeled Data Using Semisupervised Learning". Wireless Communications and Mobile Computing 2023 (20 de abril de 2023): 1–11. http://dx.doi.org/10.1155/2023/7724264.
Texto completo da fonteZhu, Yong, Yongwei Tao e Zequn Li. "Short-circuit Current-based Parametrically Identification for Doubly Fed Induction Generator". Advances in Engineering Technology Research 9, n.º 1 (27 de dezembro de 2023): 133. http://dx.doi.org/10.56028/aetr.9.1.133.2024.
Texto completo da fontePrashant Krishnan, V., S. Rajarajeswari, Venkat Krishnamohan, Vivek Chandra Sheel e R. Deepak. "Music Generation Using Deep Learning Techniques". Journal of Computational and Theoretical Nanoscience 17, n.º 9 (1 de julho de 2020): 3983–87. http://dx.doi.org/10.1166/jctn.2020.9003.
Texto completo da fonteZhu, Yancheng, Qiwei Wu e Jianzi Liu. "A Comparative Study of Contrastive Learning-Based Few-Shot Unsupervised Algorithms for Efficient Deep Learning". Journal of Physics: Conference Series 2560, n.º 1 (1 de agosto de 2023): 012048. http://dx.doi.org/10.1088/1742-6596/2560/1/012048.
Texto completo da fonteYang, Geunbo, Wongyu Lee, Youjung Seo, Choongseop Lee, Woojoon Seok, Jongkil Park, Donggyu Sim e Cheolsoo Park. "Unsupervised Spiking Neural Network with Dynamic Learning of Inhibitory Neurons". Sensors 23, n.º 16 (17 de agosto de 2023): 7232. http://dx.doi.org/10.3390/s23167232.
Texto completo da fonteSoydaner, Derya. "A Comparison of Optimization Algorithms for Deep Learning". International Journal of Pattern Recognition and Artificial Intelligence 34, n.º 13 (30 de abril de 2020): 2052013. http://dx.doi.org/10.1142/s0218001420520138.
Texto completo da fontePolanski, Jaroslaw. "Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry". International Journal of Molecular Sciences 23, n.º 5 (3 de março de 2022): 2797. http://dx.doi.org/10.3390/ijms23052797.
Texto completo da fonteWani, M. Arif, e Saduf Afzal. "Optimization of deep network models through fine tuning". International Journal of Intelligent Computing and Cybernetics 11, n.º 3 (13 de agosto de 2018): 386–403. http://dx.doi.org/10.1108/ijicc-06-2017-0070.
Texto completo da fonteLiu, MengYang, MingJun Li e XiaoYang Zhang. "The Application of the Unsupervised Migration Method Based on Deep Learning Model in the Marketing Oriented Allocation of High Level Accounting Talents". Computational Intelligence and Neuroscience 2022 (6 de junho de 2022): 1–10. http://dx.doi.org/10.1155/2022/5653942.
Texto completo da fonteLiu, MengYang, MingJun Li e XiaoYang Zhang. "The Application of the Unsupervised Migration Method Based on Deep Learning Model in the Marketing Oriented Allocation of High Level Accounting Talents". Computational Intelligence and Neuroscience 2022 (6 de junho de 2022): 1–10. http://dx.doi.org/10.1155/2022/5653942.
Texto completo da fonteCheerla, Anika, e Olivier Gevaert. "Deep learning with multimodal representation for pancancer prognosis prediction". Bioinformatics 35, n.º 14 (julho de 2019): i446—i454. http://dx.doi.org/10.1093/bioinformatics/btz342.
Texto completo da fonteZaveri, Zainab, Dhruv Gosain e Arul Prakash M. "Optical Compute Engine Using Deep CNN". International Journal of Engineering & Technology 7, n.º 2.24 (25 de abril de 2018): 541. http://dx.doi.org/10.14419/ijet.v7i2.24.12157.
Texto completo da fonteLi, Jinlong, Xiaochen Yuan, Jinfeng Li, Guoheng Huang, Ping Li e Li Feng. "CD-SDN: Unsupervised Sensitivity Disparity Networks for Hyper-Spectral Image Change Detection". Remote Sensing 14, n.º 19 (26 de setembro de 2022): 4806. http://dx.doi.org/10.3390/rs14194806.
Texto completo da fonteZhu, Chang-Hao, e Jie Zhang. "Developing Soft Sensors for Polymer Melt Index in an Industrial Polymerization Process Using Deep Belief Networks". International Journal of Automation and Computing 17, n.º 1 (5 de novembro de 2019): 44–54. http://dx.doi.org/10.1007/s11633-019-1203-x.
Texto completo da fonteHoernle, Nick, Rafael Michael Karampatsis, Vaishak Belle e Kobi Gal. "MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 5 (28 de junho de 2022): 5700–5709. http://dx.doi.org/10.1609/aaai.v36i5.20512.
Texto completo da fonteLi, Xuelong, Zhenghang Yuan e Qi Wang. "Unsupervised Deep Noise Modeling for Hyperspectral Image Change Detection". Remote Sensing 11, n.º 3 (28 de janeiro de 2019): 258. http://dx.doi.org/10.3390/rs11030258.
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