Artigos de revistas sobre o tema "Sparse deep neural networks"
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Scardapane, Simone, Danilo Comminiello, Amir Hussain e Aurelio Uncini. "Group sparse regularization for deep neural networks". Neurocomputing 241 (junho de 2017): 81–89. http://dx.doi.org/10.1016/j.neucom.2017.02.029.
Texto completo da fonteZang, Ke, Wenqi Wu e Wei Luo. "Deep Sparse Learning for Automatic Modulation Classification Using Recurrent Neural Networks". Sensors 21, n.º 19 (25 de setembro de 2021): 6410. http://dx.doi.org/10.3390/s21196410.
Texto completo da fonteWu, Kailun, Yiwen Guo e Changshui Zhang. "Compressing Deep Neural Networks With Sparse Matrix Factorization". IEEE Transactions on Neural Networks and Learning Systems 31, n.º 10 (outubro de 2020): 3828–38. http://dx.doi.org/10.1109/tnnls.2019.2946636.
Texto completo da fonteGangopadhyay, Briti, Pallab Dasgupta e Soumyajit Dey. "Safety Aware Neural Pruning for Deep Reinforcement Learning (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 13 (26 de junho de 2023): 16212–13. http://dx.doi.org/10.1609/aaai.v37i13.26966.
Texto completo da fontePetschenig, Horst, e Robert Legenstein. "Quantized rewiring: hardware-aware training of sparse deep neural networks". Neuromorphic Computing and Engineering 3, n.º 2 (26 de maio de 2023): 024006. http://dx.doi.org/10.1088/2634-4386/accd8f.
Texto completo da fonteBelay, Kaleab. "Gradient and Mangitude Based Pruning for Sparse Deep Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 11 (28 de junho de 2022): 13126–27. http://dx.doi.org/10.1609/aaai.v36i11.21699.
Texto completo da fonteKaur, Mandeep, e Pradip Kumar Yadava. "A Review on Classification of Images with Convolutional Neural Networks". International Journal for Research in Applied Science and Engineering Technology 11, n.º 7 (31 de julho de 2023): 658–63. http://dx.doi.org/10.22214/ijraset.2023.54704.
Texto completo da fonteBi, Jia, e Steve R. Gunn. "Sparse Deep Neural Network Optimization for Embedded Intelligence". International Journal on Artificial Intelligence Tools 29, n.º 03n04 (junho de 2020): 2060002. http://dx.doi.org/10.1142/s0218213020600027.
Texto completo da fonteGallicchio, Claudio, e Alessio Micheli. "Fast and Deep Graph Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 3898–905. http://dx.doi.org/10.1609/aaai.v34i04.5803.
Texto completo da fonteTartaglione, Enzo, Andrea Bragagnolo, Attilio Fiandrotti e Marco Grangetto. "LOss-Based SensiTivity rEgulaRization: Towards deep sparse neural networks". Neural Networks 146 (fevereiro de 2022): 230–37. http://dx.doi.org/10.1016/j.neunet.2021.11.029.
Texto completo da fonteMa, Rongrong, Jianyu Miao, Lingfeng Niu e Peng Zhang. "Transformed ℓ1 regularization for learning sparse deep neural networks". Neural Networks 119 (novembro de 2019): 286–98. http://dx.doi.org/10.1016/j.neunet.2019.08.015.
Texto completo da fonteZhao, Jin, e Licheng Jiao. "Fast Sparse Deep Neural Networks: Theory and Performance Analysis". IEEE Access 7 (2019): 74040–55. http://dx.doi.org/10.1109/access.2019.2920688.
Texto completo da fonteKarim, Ahmad M., Mehmet S. Güzel, Mehmet R. Tolun, Hilal Kaya e Fatih V. Çelebi. "A New Generalized Deep Learning Framework Combining Sparse Autoencoder and Taguchi Method for Novel Data Classification and Processing". Mathematical Problems in Engineering 2018 (7 de junho de 2018): 1–13. http://dx.doi.org/10.1155/2018/3145947.
Texto completo da fonteLi, Yihang. "Sparse-Aware Deep Learning Accelerator". Highlights in Science, Engineering and Technology 39 (1 de abril de 2023): 305–10. http://dx.doi.org/10.54097/hset.v39i.6544.
Texto completo da fonteOhn, Ilsang, e Yongdai Kim. "Nonconvex Sparse Regularization for Deep Neural Networks and Its Optimality". Neural Computation 34, n.º 2 (14 de janeiro de 2022): 476–517. http://dx.doi.org/10.1162/neco_a_01457.
Texto completo da fonteAvgerinos, Christos, Nicholas Vretos e Petros Daras. "Less Is More: Adaptive Trainable Gradient Dropout for Deep Neural Networks". Sensors 23, n.º 3 (24 de janeiro de 2023): 1325. http://dx.doi.org/10.3390/s23031325.
Texto completo da fonteHao, Yutong, Yunpeng Liu, Jinmiao Zhao e Chuang Yu. "Dual-Domain Prior-Driven Deep Network for Infrared Small-Target Detection". Remote Sensing 15, n.º 15 (31 de julho de 2023): 3827. http://dx.doi.org/10.3390/rs15153827.
Texto completo da fonteLee, Sangkyun, e Jeonghyun Lee. "Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems". Applied Sciences 9, n.º 8 (23 de abril de 2019): 1669. http://dx.doi.org/10.3390/app9081669.
Texto completo da fonteMousavi, Hamid, Mohammad Loni, Mina Alibeigi e Masoud Daneshtalab. "DASS: Differentiable Architecture Search for Sparse Neural Networks". ACM Transactions on Embedded Computing Systems 22, n.º 5s (9 de setembro de 2023): 1–21. http://dx.doi.org/10.1145/3609385.
Texto completo da fonteAo, Ren, Zhang Tao, Wang Yuhao, Lin Sheng, Dong Peiyan, Chen Yen-kuang, Xie Yuan e Wang Yanzhi. "DARB: A Density-Adaptive Regular-Block Pruning for Deep Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 5495–502. http://dx.doi.org/10.1609/aaai.v34i04.6000.
Texto completo da fonteÖstling, Robert. "Part of Speech Tagging: Shallow or Deep Learning?" Northern European Journal of Language Technology 5 (19 de junho de 2018): 1–15. http://dx.doi.org/10.3384/nejlt.2000-1533.1851.
Texto completo da fonteGong, Maoguo, Jia Liu, Hao Li, Qing Cai e Linzhi Su. "A Multiobjective Sparse Feature Learning Model for Deep Neural Networks". IEEE Transactions on Neural Networks and Learning Systems 26, n.º 12 (dezembro de 2015): 3263–77. http://dx.doi.org/10.1109/tnnls.2015.2469673.
Texto completo da fonteBoo, Yoonho, e Wonyong Sung. "Compression of Deep Neural Networks with Structured Sparse Ternary Coding". Journal of Signal Processing Systems 91, n.º 9 (6 de novembro de 2018): 1009–19. http://dx.doi.org/10.1007/s11265-018-1418-z.
Texto completo da fonteZhao, Yao, Qingsong Liu, He Tian, Bingo Wing-Kuen Ling e Zhe Zhang. "DeepRED Based Sparse SAR Imaging". Remote Sensing 16, n.º 2 (5 de janeiro de 2024): 212. http://dx.doi.org/10.3390/rs16020212.
Texto completo da fonteWan, Xinyue, Bofeng Zhang, Guobing Zou e Furong Chang. "Sparse Data Recommendation by Fusing Continuous Imputation Denoising Autoencoder and Neural Matrix Factorization". Applied Sciences 9, n.º 1 (24 de dezembro de 2018): 54. http://dx.doi.org/10.3390/app9010054.
Texto completo da fonteEl-Yabroudi, Mohammad Z., Ikhlas Abdel-Qader, Bradley J. Bazuin, Osama Abudayyeh e Rakan C. Chabaan. "Guided Depth Completion with Instance Segmentation Fusion in Autonomous Driving Applications". Sensors 22, n.º 24 (7 de dezembro de 2022): 9578. http://dx.doi.org/10.3390/s22249578.
Texto completo da fonteQiao, Chen, Yan Shi, Yu-Xian Diao, Vince D. Calhoun e Yu-Ping Wang. "Log-sum enhanced sparse deep neural network". Neurocomputing 407 (setembro de 2020): 206–20. http://dx.doi.org/10.1016/j.neucom.2020.04.118.
Texto completo da fonteMorotti, Elena, Davide Evangelista e Elena Loli Piccolomini. "A Green Prospective for Learned Post-Processing in Sparse-View Tomographic Reconstruction". Journal of Imaging 7, n.º 8 (7 de agosto de 2021): 139. http://dx.doi.org/10.3390/jimaging7080139.
Texto completo da fonteWan, Lulu, Tao Chen, Antonio Plaza e Haojie Cai. "Hyperspectral Unmixing Based on Spectral and Sparse Deep Convolutional Neural Networks". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021): 11669–82. http://dx.doi.org/10.1109/jstars.2021.3126755.
Texto completo da fonteKhattak, Muhammad Irfan, Nasir Saleem, Jiechao Gao, Elena Verdu e Javier Parra Fuente. "Regularized sparse features for noisy speech enhancement using deep neural networks". Computers and Electrical Engineering 100 (maio de 2022): 107887. http://dx.doi.org/10.1016/j.compeleceng.2022.107887.
Texto completo da fonteXie, Zhihua, Yi Li, Jieyi Niu, Ling Shi, Zhipeng Wang e Guoyu Lu. "Hyperspectral face recognition based on sparse spectral attention deep neural networks". Optics Express 28, n.º 24 (16 de novembro de 2020): 36286. http://dx.doi.org/10.1364/oe.404793.
Texto completo da fonteLiu, Wei, Yue Yang e Longsheng Wei. "Weather Recognition of Street Scene Based on Sparse Deep Neural Networks". Journal of Advanced Computational Intelligence and Intelligent Informatics 21, n.º 3 (19 de maio de 2017): 403–8. http://dx.doi.org/10.20965/jaciii.2017.p0403.
Texto completo da fonteSchwab, Johannes, Stephan Antholzer e Markus Haltmeier. "Big in Japan: Regularizing Networks for Solving Inverse Problems". Journal of Mathematical Imaging and Vision 62, n.º 3 (3 de outubro de 2019): 445–55. http://dx.doi.org/10.1007/s10851-019-00911-1.
Texto completo da fonte.., Vani, e Piyush Kumar Pareek. "Deep Multiple Instance Learning Approach for Classification in Clinical Decision Support Systems". American Journal of Business and Operations Research 10, n.º 2 (2023): 52–60. http://dx.doi.org/10.54216/ajbor.100206.
Texto completo da fonteHe, Haoyuan, Lingxuan Huang, Zisen Huang e Tiantian Yang. "The Compression Techniques Applied on Deep Learning Model". Highlights in Science, Engineering and Technology 4 (26 de julho de 2022): 325–31. http://dx.doi.org/10.54097/hset.v4i.920.
Texto completo da fonteAlmulla Khalaf, Maysa Ibrahem, e John Q. Gan. "A three-stage learning algorithm for deep multilayer perceptron with effective weight initialisation based on sparse auto-encoder". Artificial Intelligence Research 8, n.º 1 (2 de abril de 2019): 41. http://dx.doi.org/10.5430/air.v8n1p41.
Texto completo da fonteZahn, Olivia, Jorge Bustamante, Callin Switzer, Thomas L. Daniel e J. Nathan Kutz. "Pruning deep neural networks generates a sparse, bio-inspired nonlinear controller for insect flight". PLOS Computational Biology 18, n.º 9 (27 de setembro de 2022): e1010512. http://dx.doi.org/10.1371/journal.pcbi.1010512.
Texto completo da fonteLiu, Xiao, Wenbin Li, Jing Huo, Lili Yao e Yang Gao. "Layerwise Sparse Coding for Pruned Deep Neural Networks with Extreme Compression Ratio". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 4900–4907. http://dx.doi.org/10.1609/aaai.v34i04.5927.
Texto completo da fonteYao, Zhongtian, Kejie Huang, Haibin Shen e Zhaoyan Ming. "Deep Neural Network Acceleration With Sparse Prediction Layers". IEEE Access 8 (2020): 6839–48. http://dx.doi.org/10.1109/access.2020.2963941.
Texto completo da fonteLee, Gwo-Chuan, Jyun-Hong Li e Zi-Yang Li. "A Wasserstein Generative Adversarial Network–Gradient Penalty-Based Model with Imbalanced Data Enhancement for Network Intrusion Detection". Applied Sciences 13, n.º 14 (12 de julho de 2023): 8132. http://dx.doi.org/10.3390/app13148132.
Texto completo da fontePhan, Huy, Miao Yin, Yang Sui, Bo Yuan e Saman Zonouz. "CSTAR: Towards Compact and Structured Deep Neural Networks with Adversarial Robustness". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 2 (26 de junho de 2023): 2065–73. http://dx.doi.org/10.1609/aaai.v37i2.25299.
Texto completo da fonteZhang, Hongwei, Jiacheng Ni, Kaiming Li, Ying Luo e Qun Zhang. "Nonsparse SAR Scene Imaging Network Based on Sparse Representation and Approximate Observations". Remote Sensing 15, n.º 17 (22 de agosto de 2023): 4126. http://dx.doi.org/10.3390/rs15174126.
Texto completo da fonteGong, Zhenghui, Xiaolong Su, Panhe Hu, Shuowei Liu e Zhen Liu. "Deep Unfolding Sparse Bayesian Learning Network for Off-Grid DOA Estimation with Nested Array". Remote Sensing 15, n.º 22 (10 de novembro de 2023): 5320. http://dx.doi.org/10.3390/rs15225320.
Texto completo da fonteChen, Yuanyuan, e Zhang Yi. "Adaptive sparse dropout: Learning the certainty and uncertainty in deep neural networks". Neurocomputing 450 (agosto de 2021): 354–61. http://dx.doi.org/10.1016/j.neucom.2021.04.047.
Texto completo da fonteChen, Jiayu, Xiang Li, Vince D. Calhoun, Jessica A. Turner, Theo G. M. Erp, Lei Wang, Ole A. Andreassen et al. "Sparse deep neural networks on imaging genetics for schizophrenia case–control classification". Human Brain Mapping 42, n.º 8 (16 de março de 2021): 2556–68. http://dx.doi.org/10.1002/hbm.25387.
Texto completo da fonteKovacs, Mate, e Victor V. Kryssanov. "Expanding the Feature Space of Deep Neural Networks for Sentiment Classification". International Journal of Machine Learning and Computing 10, n.º 2 (fevereiro de 2020): 271–76. http://dx.doi.org/10.18178/ijmlc.2020.10.2.931.
Texto completo da fonteLui, Hugo F. S., e William R. Wolf. "Construction of reduced-order models for fluid flows using deep feedforward neural networks". Journal of Fluid Mechanics 872 (14 de junho de 2019): 963–94. http://dx.doi.org/10.1017/jfm.2019.358.
Texto completo da fonteChen, Qipeng, Qiaoqiao Xiong, Haisong Huang, Saihong Tang e Zhenghong Liu. "Research on the Construction of an Efficient and Lightweight Online Detection Method for Tiny Surface Defects through Model Compression and Knowledge Distillation". Electronics 13, n.º 2 (5 de janeiro de 2024): 253. http://dx.doi.org/10.3390/electronics13020253.
Texto completo da fonteZhao, Yao, Chengwen Ou, He Tian, Bingo Wing-Kuen Ling, Ye Tian e Zhe Zhang. "Sparse SAR Imaging Algorithm in Marine Environments Based on Memory-Augmented Deep Unfolding Network". Remote Sensing 16, n.º 7 (5 de abril de 2024): 1289. http://dx.doi.org/10.3390/rs16071289.
Texto completo da fonteKohjima, Masahiro. "Shuffled Deep Regression". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 12 (24 de março de 2024): 13238–45. http://dx.doi.org/10.1609/aaai.v38i12.29224.
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