Artykuły w czasopismach na temat „Unsupervised deep neural networks”
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Banzi, Jamal, Isack Bulugu i Zhongfu Ye. "Deep Predictive Neural Network: Unsupervised Learning for Hand Pose Estimation". International Journal of Machine Learning and Computing 9, nr 4 (sierpień 2019): 432–39. http://dx.doi.org/10.18178/ijmlc.2019.9.4.822.
Pełny tekst źródłaGuo, Wenqi, Weixiong Zhang, Zheng Zhang, Ping Tang i Shichen Gao. "Deep Temporal Iterative Clustering for Satellite Image Time Series Land Cover Analysis". Remote Sensing 14, nr 15 (29.07.2022): 3635. http://dx.doi.org/10.3390/rs14153635.
Pełny tekst źródłaXu, Jianqiao, Zhaolu Zuo, Danchao Wu, Bing Li, Xiaoni Li i Deyi Kong. "Bearing Defect Detection with Unsupervised Neural Networks". Shock and Vibration 2021 (19.08.2021): 1–11. http://dx.doi.org/10.1155/2021/9544809.
Pełny tekst źródłaFeng, Yu, i Hui Sun. "Basketball Footwork and Application Supported by Deep Learning Unsupervised Transfer Method". International Journal of Information Technology and Web Engineering 18, nr 1 (1.12.2023): 1–17. http://dx.doi.org/10.4018/ijitwe.334365.
Pełny tekst źródłaSun, Yanan, Gary G. Yen i Zhang Yi. "Evolving Unsupervised Deep Neural Networks for Learning Meaningful Representations". IEEE Transactions on Evolutionary Computation 23, nr 1 (luty 2019): 89–103. http://dx.doi.org/10.1109/tevc.2018.2808689.
Pełny tekst źródłaShi, Yu, Cien Fan, Lian Zou, Caixia Sun i Yifeng Liu. "Unsupervised Adversarial Defense through Tandem Deep Image Priors". Electronics 9, nr 11 (19.11.2020): 1957. http://dx.doi.org/10.3390/electronics9111957.
Pełny tekst źródłaThakur, Amey. "Generative Adversarial Networks". International Journal for Research in Applied Science and Engineering Technology 9, nr 8 (31.08.2021): 2307–25. http://dx.doi.org/10.22214/ijraset.2021.37723.
Pełny tekst źródłaFerles, Christos, Yannis Papanikolaou, Stylianos P. Savaidis i Stelios A. Mitilineos. "Deep Self-Organizing Map of Convolutional Layers for Clustering and Visualizing Image Data". Machine Learning and Knowledge Extraction 3, nr 4 (14.11.2021): 879–99. http://dx.doi.org/10.3390/make3040044.
Pełny tekst źródłaZhuang, Chengxu, Siming Yan, Aran Nayebi, Martin Schrimpf, Michael C. Frank, James J. DiCarlo i Daniel L. K. Yamins. "Unsupervised neural network models of the ventral visual stream". Proceedings of the National Academy of Sciences 118, nr 3 (11.01.2021): e2014196118. http://dx.doi.org/10.1073/pnas.2014196118.
Pełny tekst źródłaLin, Baihan. "Regularity Normalization: Neuroscience-Inspired Unsupervised Attention across Neural Network Layers". Entropy 24, nr 1 (28.12.2021): 59. http://dx.doi.org/10.3390/e24010059.
Pełny tekst źródłaAbiyev, Rahib H., i Mohammad Khaleel Sallam Ma’aitah. "Deep Convolutional Neural Networks for Chest Diseases Detection". Journal of Healthcare Engineering 2018 (1.08.2018): 1–11. http://dx.doi.org/10.1155/2018/4168538.
Pełny tekst źródłaBrowne, David, Michael Giering i Steven Prestwich. "PulseNetOne: Fast Unsupervised Pruning of Convolutional Neural Networks for Remote Sensing". Remote Sensing 12, nr 7 (29.03.2020): 1092. http://dx.doi.org/10.3390/rs12071092.
Pełny tekst źródłaYi, Cheng. "Application of Convolutional Networks in Clothing Design from the Perspective of Deep Learning". Scientific Programming 2022 (27.09.2022): 1–8. http://dx.doi.org/10.1155/2022/6173981.
Pełny tekst źródłaGhosh, Saheb, Sathis Kumar B i Kathir Deivanai. "DETECTION OF WHALES USING DEEP LEARNING METHODS AND NEURAL NETWORKS". Asian Journal of Pharmaceutical and Clinical Research 10, nr 13 (1.04.2017): 489. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.20767.
Pełny tekst źródłaSolomon, Enoch, Abraham Woubie i Krzysztof J. Cios. "UFace: An Unsupervised Deep Learning Face Verification System". Electronics 11, nr 23 (26.11.2022): 3909. http://dx.doi.org/10.3390/electronics11233909.
Pełny tekst źródłaAltuntas, Volkan. "NodeVector: A Novel Network Node Vectorization with Graph Analysis and Deep Learning". Applied Sciences 14, nr 2 (16.01.2024): 775. http://dx.doi.org/10.3390/app14020775.
Pełny tekst źródłaMa, Chao, Yun Gu, Chen Gong, Jie Yang i Deying Feng. "Unsupervised Video Hashing via Deep Neural Network". Neural Processing Letters 47, nr 3 (17.03.2018): 877–90. http://dx.doi.org/10.1007/s11063-018-9812-x.
Pełny tekst źródłaNaidu, D. J. Samatha, i T. Mahammad Rafi. "HANDWRITTEN CHARACTER RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS". International Journal of Computer Science and Mobile Computing 10, nr 8 (30.08.2021): 41–45. http://dx.doi.org/10.47760/ijcsmc.2021.v10i08.007.
Pełny tekst źródłaHu, Ruiqi, Shirui Pan, Guodong Long, Qinghua Lu, Liming Zhu i Jing Jiang. "Going Deep: Graph Convolutional Ladder-Shape Networks". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 03 (3.04.2020): 2838–45. http://dx.doi.org/10.1609/aaai.v34i03.5673.
Pełny tekst źródłaHuang, Qiuyuan, Li Deng, Dapeng Wu, Chang Liu i Xiaodong He. "Attentive Tensor Product Learning". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17.07.2019): 1344–51. http://dx.doi.org/10.1609/aaai.v33i01.33011344.
Pełny tekst źródłaHuang, Jiabo, Qi Dong, Shaogang Gong i Xiatian Zhu. "Unsupervised Deep Learning via Affinity Diffusion". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 07 (3.04.2020): 11029–36. http://dx.doi.org/10.1609/aaai.v34i07.6757.
Pełny tekst źródłaTyshchenko, Vitalii. "ANALYSIS OF TRAINING METHODS AND NEURAL NETWORK TOOLS FOR FAKE NEWS DETECTION". Cybersecurity: Education, Science, Technique 4, nr 20 (2023): 20–34. http://dx.doi.org/10.28925/2663-4023.2023.20.2034.
Pełny tekst źródłaHeo, Seongmin, i Jay H. Lee. "Statistical Process Monitoring of the Tennessee Eastman Process Using Parallel Autoassociative Neural Networks and a Large Dataset". Processes 7, nr 7 (1.07.2019): 411. http://dx.doi.org/10.3390/pr7070411.
Pełny tekst źródłaCao, Yanpeng, Dayan Guan, Weilin Huang, Jiangxin Yang, Yanlong Cao i Yu Qiao. "Pedestrian detection with unsupervised multispectral feature learning using deep neural networks". Information Fusion 46 (marzec 2019): 206–17. http://dx.doi.org/10.1016/j.inffus.2018.06.005.
Pełny tekst źródłaZhang, Pengfei, i Xiaoming Ju. "Adversarial Sample Detection with Gaussian Mixture Conditional Generative Adversarial Networks". Mathematical Problems in Engineering 2021 (13.09.2021): 1–18. http://dx.doi.org/10.1155/2021/8268249.
Pełny tekst źródłaKhodayar, Mahdi, i Jacob Regan. "Deep Neural Networks in Power Systems: A Review". Energies 16, nr 12 (17.06.2023): 4773. http://dx.doi.org/10.3390/en16124773.
Pełny tekst źródłaLe Roux, Nicolas, i Yoshua Bengio. "Deep Belief Networks Are Compact Universal Approximators". Neural Computation 22, nr 8 (sierpień 2010): 2192–207. http://dx.doi.org/10.1162/neco.2010.08-09-1081.
Pełny tekst źródłaZhu, Yi, Xinke Zhou i Xindong Wu. "Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder". Applied Sciences 13, nr 1 (29.12.2022): 481. http://dx.doi.org/10.3390/app13010481.
Pełny tekst źródłaLin, Yi-Nan, Tsang-Yen Hsieh, Cheng-Ying Yang, Victor RL Shen, Tony Tong-Ying Juang i Wen-Hao Chen. "Deep Petri nets of unsupervised and supervised learning". Measurement and Control 53, nr 7-8 (9.06.2020): 1267–77. http://dx.doi.org/10.1177/0020294020923375.
Pełny tekst źródłaSewani, Harshini, i Rasha Kashef. "An Autoencoder-Based Deep Learning Classifier for Efficient Diagnosis of Autism". Children 7, nr 10 (14.10.2020): 182. http://dx.doi.org/10.3390/children7100182.
Pełny tekst źródłaAjay, P., B. Nagaraj, R. Arun Kumar, Ruihang Huang i P. Ananthi. "Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm". Scanning 2022 (6.06.2022): 1–9. http://dx.doi.org/10.1155/2022/1200860.
Pełny tekst źródłaMamun, Abdullah Al, Em Poh Ping, Jakir Hossen, Anik Tahabilder i Busrat Jahan. "A Comprehensive Review on Lane Marking Detection Using Deep Neural Networks". Sensors 22, nr 19 (10.10.2022): 7682. http://dx.doi.org/10.3390/s22197682.
Pełny tekst źródłaVélez, Paulina, Manuel Miranda, Carmen Serrano i Begoña Acha. "Does a Previous Segmentation Improve the Automatic Detection of Basal Cell Carcinoma Using Deep Neural Networks?" Applied Sciences 12, nr 4 (17.02.2022): 2092. http://dx.doi.org/10.3390/app12042092.
Pełny tekst źródłaChu, Lei, Hao Pan i Wenping Wang. "Unsupervised Shape Completion via Deep Prior in the Neural Tangent Kernel Perspective". ACM Transactions on Graphics 40, nr 3 (4.07.2021): 1–17. http://dx.doi.org/10.1145/3459234.
Pełny tekst źródłaLi, Yibing, Sitong Zhang, Xiang Li i Fang Ye. "Remote Sensing Image Classification with Few Labeled Data Using Semisupervised Learning". Wireless Communications and Mobile Computing 2023 (20.04.2023): 1–11. http://dx.doi.org/10.1155/2023/7724264.
Pełny tekst źródłaZhu, Yong, Yongwei Tao i Zequn Li. "Short-circuit Current-based Parametrically Identification for Doubly Fed Induction Generator". Advances in Engineering Technology Research 9, nr 1 (27.12.2023): 133. http://dx.doi.org/10.56028/aetr.9.1.133.2024.
Pełny tekst źródłaPrashant Krishnan, V., S. Rajarajeswari, Venkat Krishnamohan, Vivek Chandra Sheel i R. Deepak. "Music Generation Using Deep Learning Techniques". Journal of Computational and Theoretical Nanoscience 17, nr 9 (1.07.2020): 3983–87. http://dx.doi.org/10.1166/jctn.2020.9003.
Pełny tekst źródłaZhu, Yancheng, Qiwei Wu i Jianzi Liu. "A Comparative Study of Contrastive Learning-Based Few-Shot Unsupervised Algorithms for Efficient Deep Learning". Journal of Physics: Conference Series 2560, nr 1 (1.08.2023): 012048. http://dx.doi.org/10.1088/1742-6596/2560/1/012048.
Pełny tekst źródłaYang, Geunbo, Wongyu Lee, Youjung Seo, Choongseop Lee, Woojoon Seok, Jongkil Park, Donggyu Sim i Cheolsoo Park. "Unsupervised Spiking Neural Network with Dynamic Learning of Inhibitory Neurons". Sensors 23, nr 16 (17.08.2023): 7232. http://dx.doi.org/10.3390/s23167232.
Pełny tekst źródłaSoydaner, Derya. "A Comparison of Optimization Algorithms for Deep Learning". International Journal of Pattern Recognition and Artificial Intelligence 34, nr 13 (30.04.2020): 2052013. http://dx.doi.org/10.1142/s0218001420520138.
Pełny tekst źródłaPolanski, Jaroslaw. "Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry". International Journal of Molecular Sciences 23, nr 5 (3.03.2022): 2797. http://dx.doi.org/10.3390/ijms23052797.
Pełny tekst źródłaWani, M. Arif, i Saduf Afzal. "Optimization of deep network models through fine tuning". International Journal of Intelligent Computing and Cybernetics 11, nr 3 (13.08.2018): 386–403. http://dx.doi.org/10.1108/ijicc-06-2017-0070.
Pełny tekst źródłaLiu, MengYang, MingJun Li i 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 (6.06.2022): 1–10. http://dx.doi.org/10.1155/2022/5653942.
Pełny tekst źródłaLiu, MengYang, MingJun Li i 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 (6.06.2022): 1–10. http://dx.doi.org/10.1155/2022/5653942.
Pełny tekst źródłaCheerla, Anika, i Olivier Gevaert. "Deep learning with multimodal representation for pancancer prognosis prediction". Bioinformatics 35, nr 14 (lipiec 2019): i446—i454. http://dx.doi.org/10.1093/bioinformatics/btz342.
Pełny tekst źródłaZaveri, Zainab, Dhruv Gosain i Arul Prakash M. "Optical Compute Engine Using Deep CNN". International Journal of Engineering & Technology 7, nr 2.24 (25.04.2018): 541. http://dx.doi.org/10.14419/ijet.v7i2.24.12157.
Pełny tekst źródłaLi, Jinlong, Xiaochen Yuan, Jinfeng Li, Guoheng Huang, Ping Li i Li Feng. "CD-SDN: Unsupervised Sensitivity Disparity Networks for Hyper-Spectral Image Change Detection". Remote Sensing 14, nr 19 (26.09.2022): 4806. http://dx.doi.org/10.3390/rs14194806.
Pełny tekst źródłaZhu, Chang-Hao, i 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, nr 1 (5.11.2019): 44–54. http://dx.doi.org/10.1007/s11633-019-1203-x.
Pełny tekst źródłaHoernle, Nick, Rafael Michael Karampatsis, Vaishak Belle i Kobi Gal. "MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 36, nr 5 (28.06.2022): 5700–5709. http://dx.doi.org/10.1609/aaai.v36i5.20512.
Pełny tekst źródłaLi, Xuelong, Zhenghang Yuan i Qi Wang. "Unsupervised Deep Noise Modeling for Hyperspectral Image Change Detection". Remote Sensing 11, nr 3 (28.01.2019): 258. http://dx.doi.org/10.3390/rs11030258.
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