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Статті в журналах з теми "Neural Tensor Network"
Gao, Yuan, Laurence T. Yang, Dehua Zheng, Jing Yang, and Yaliang Zhao. "Quantized Tensor Neural Network." ACM/IMS Transactions on Data Science 2, no. 4 (November 30, 2021): 1–18. http://dx.doi.org/10.1145/3491255.
Повний текст джерелаMURTHY, GARIMELLA RAMA. "MULTI/INFINITE DIMENSIONAL NEURAL NETWORKS, MULTI/INFINITE DIMENSIONAL LOGIC THEORY." International Journal of Neural Systems 15, no. 03 (June 2005): 223–35. http://dx.doi.org/10.1142/s0129065705000190.
Повний текст джерелаFeng, Yu, Xianfeng Xu, and Yun Meng. "Short-Term Load Forecasting with Tensor Partial Least Squares-Neural Network." Energies 12, no. 6 (March 14, 2019): 990. http://dx.doi.org/10.3390/en12060990.
Повний текст джерелаXu, Zenglin. "Tensor Networks Meet Neural Networks." Journal of Physics: Conference Series 2278, no. 1 (May 1, 2022): 012003. http://dx.doi.org/10.1088/1742-6596/2278/1/012003.
Повний текст джерелаSobolev, Konstantin, Dmitry Ermilov, Anh-Huy Phan, and Andrzej Cichocki. "PARS: Proxy-Based Automatic Rank Selection for Neural Network Compression via Low-Rank Weight Approximation." Mathematics 10, no. 20 (October 14, 2022): 3801. http://dx.doi.org/10.3390/math10203801.
Повний текст джерелаWang, Xuezhong, Maolin Che, and Yimin Wei. "Tensor neural network models for tensor singular value decompositions." Computational Optimization and Applications 75, no. 3 (January 20, 2020): 753–77. http://dx.doi.org/10.1007/s10589-020-00167-1.
Повний текст джерелаZhan, Tianming, Bo Song, Yang Xu, Minghua Wan, Xin Wang, Guowei Yang, and Zebin Wu. "SSCNN-S: A Spectral-Spatial Convolution Neural Network with Siamese Architecture for Change Detection." Remote Sensing 13, no. 5 (February 27, 2021): 895. http://dx.doi.org/10.3390/rs13050895.
Повний текст джерелаHayashi, Kohei. "Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks." Brain & Neural Networks 29, no. 4 (December 5, 2022): 193–201. http://dx.doi.org/10.3902/jnns.29.193.
Повний текст джерелаLing, Julia, Andrew Kurzawski, and Jeremy Templeton. "Reynolds averaged turbulence modelling using deep neural networks with embedded invariance." Journal of Fluid Mechanics 807 (October 18, 2016): 155–66. http://dx.doi.org/10.1017/jfm.2016.615.
Повний текст джерелаHameed, Marawan Gamal Abdel, Marzieh S. Tahaei, Ali Mosleh, and Vahid Partovi Nia. "Convolutional Neural Network Compression through Generalized Kronecker Product Decomposition." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 771–79. http://dx.doi.org/10.1609/aaai.v36i1.19958.
Повний текст джерелаДисертації з теми "Neural Tensor Network"
Teng, Peiyuan. "Tensor network and neural network methods in physical systems." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1524836522115804.
Повний текст джерелаBhogi, Keerthana. "Two New Applications of Tensors to Machine Learning for Wireless Communications." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104970.
Повний текст джерелаMaster of Science
The increase in the number of wireless and mobile devices have led to the generation of massive amounts of multi-modal data at the users in various real-world applications including wireless communications. This has led to an increasing interest in machine learning (ML)-based data-driven techniques for communication system design. The native setting of ML is {em centralized} where all the data is available on a single device. However, the distributed nature of the users and their data has also motivated the development of distributed ML techniques. Since the success of ML techniques is grounded in their data-based nature, there is a need to maintain the efficiency and scalability of the algorithms to manage the large-scale data. Tensors are multi-dimensional arrays that provide an integrated way of representing multi-modal data. Tensor algebra and tensor decompositions have enabled the extension of several classical ML techniques to tensors-based ML techniques in various application domains such as computer vision, data-mining, image processing, and wireless communications. Tensors-based ML techniques have shown to improve the performance of the ML models because of their ability to leverage the underlying structural information in the data. In this thesis, we present two new applications of tensors to ML for wireless applications and show how the tensor structure of the concerned data can be exploited and incorporated in different ways. The first contribution is a tensor learning-based precoder codebook design technique for full-dimension multiple-input multiple-output (FD-MIMO) systems where we develop a scheme for designing low-complexity product precoder codebooks by identifying and leveraging a tensor representation of the FD-MIMO channel. The second contribution is a tensor-based gradient communication scheme for a decentralized ML technique known as federated learning (FL) with convolutional neural networks (CNNs), where we design a novel bandwidth-efficient gradient compression-reconstruction algorithm that leverages a tensor structure of the convolutional gradients. The numerical simulations in both applications demonstrate that exploiting the underlying tensor structure in the data provides significant gains in their respective performance criteria.
Rajbhandari, Samyam. "Locality Optimizations for Regular and Irregular Applications." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1469033289.
Повний текст джерелаKuchar, Olga Anna. "Development of animated finger movements via a neural network for tendon tension control." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq39322.pdf.
Повний текст джерелаChoi, Ki Sueng. "Characterizing structural neural networks in major depressive disorder using diffusion tensor imaging." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50353.
Повний текст джерелаElhag, Taha Mahmoud Salih. "Tender price modelling : artificial neural networks and regression techniques." Thesis, University of Liverpool, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.400240.
Повний текст джерелаChen, Cong. "High-Dimensional Generative Models for 3D Perception." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103948.
Повний текст джерелаDoctor of Philosophy
The development of automation systems and robotics brought the modern world unrivaled affluence and convenience. However, the current automated tasks are mainly simple repetitive motions. Tasks that require more artificial capability with advanced visual cognition are still an unsolved problem for automation. Many of the high-level cognition-based tasks require the accurate visual perception of the environment and dynamic objects from the data received from the optical sensor. The capability to represent, identify and interpret complex visual data for understanding the geometric structure of the world is 3D perception. To better tackle the existing 3D perception challenges, this dissertation proposed a set of generative learning-based frameworks on sparse tensor data for various high-dimensional robotics perception applications: underwater point cloud filtering, image restoration, deformation detection, and localization. Underwater point cloud data is relevant for many applications such as environmental monitoring or geological exploration. The data collected with sonar sensors are however subjected to different types of noise, including holes, noise measurements, and outliers. In the first chapter, we propose a generative model for point cloud data recovery using Variational Bayesian (VB) based sparse tensor factorization methods to tackle these three defects simultaneously. In the second part of the dissertation, we propose an image restoration technique to tackle missing data, which is essential for many perception applications. An efficient generative chaotic RNN framework has been introduced for recovering the sparse tensor from a single corrupted image for various types of missing data. In the last chapter, a multi-level CNN for high-dimension tensor feature extraction for underwater vehicle localization has been proposed.
Liu, Jingrong. "Design and Analysis of Intelligent Fuzzy Tension Controllers for Rolling Mills." Thesis, University of Waterloo, 2002. http://hdl.handle.net/10012/848.
Повний текст джерелаMelo, Mirthys Marinho do Carmo. "Modelagem baseada em redes neurais de meios de produção de biossurfactantes." Universidade Católica de Pernambuco, 2011. http://tede2.unicap.br:8080/handle/tede/608.
Повний текст джерелаThe success of artificial neural networks (ANN) applications as an alternative modeling technique to response surface methodology (RSM) has attracted interest from major industries such as pharmaceuticals, cosmetics, oil, food, petroleum and surfactants, among others. Development of production media is a strategic area for the industry of biosurfactants by to increase efficiency and reduce costs of the process. In this area, surface tension measurements and emulsification activity has been routinely used for indirect monitoring of biosurfactant production. In this paper, the capabilities of RNA-based modeling and MSR were compared in surface tension estimation of biosurfactant production media. The two techniques used experimental data from the central composite design with four axial points and three replicates at the central point. The concentrations of ammonium sulfate and potassium monobasic phosphate were used as independent variables. The surface tensions of cell-free broths, with 96 h, of biosurfactant production media by Candida lipolytica UCP 988 in sea water were used as response variable. The results demonstrated the superiority of the RNA-based methodology. The quadratic model obtained using MSR showed a coefficient of determination equal to 0.43 and highly significant lack of fit. The fit of the model RNA based on experimental data was excellent. Simulations with the model using the training, validation an test sets showed root mean squared error (rmse) of less than 0.05 and coefficients of determination higher than 0.99. In this context, the RNA-based estimation of surface tension from the constituents of biosurfactant production media showed to be an efficient, reliable and economical method to monitor the biosurfactant production. The work also showed the ability of the yeast Candida lipolytica UCP 0988 use corn oil and produce biosurfactants in extremely alkaline sea water (initial pH 14), supplemented with sources of nitrogen and phosphorus
O sucesso das aplicações de redes neurais artificiais (RNA) como técnica de modelagem alternativa à metodologia de superfície de resposta (MSR) tem atraído o interesse de grandes indústrias, como a farmacêutica, a de cosméticos, a de alimentos, a de petróleo e a de surfactantes, entre outras. Desenvolvimento de meios de produção é uma área estratégica para a indústria de biossurfactantes por aumentar a eficiência e reduzir custos do processo. Nesta área, determinações de tensão superficial e de atividade de emulsificação vem sendo usadas rotineiramente para monitoramento indireto da produção de biossurfactantes. No presente trabalho, as capacidades de modelagem de metodologia baseada em RNA e metodologia de superfície de resposta foram comparadas na estimação de tensão superficial de meios de produção de biossurfactante. As duas técnicas usaram dados experimentais obtidos de planejamento composto central, com 4 pontos axiais e 3 repetições no ponto central, tendo as concentrações de sulfato de amônio e fosfato monobásico de potássio como variáveis independentes e como variável resposta a tensão superficial de líquidos metabólicos, com 96 horas, livres de células, de meios de produção de biossurfactante por Candida lipolytica UCP 988. Os resultados demonstraram a superioridade da metodologia baseada em RNA. O modelo quadrático obtido usando MSR apresentou coeficiente de determinação igual a 0,43 e falta de ajuste altamente significativa. O ajuste do modelo baseado em RNA aos dados experimentais foi excelente. Simulações com o modelo usando os conjuntos de treinamento, validação e teste apresentaram raízes dos erros quadráticos médios (rmse) inferiores a 0,05 e coeficientes de determinação superiores a 0,99. Neste contexto, a estimação da tensão superficial baseada em RNA a partir dos constituintes de meios de produção de biossurfactantes mostrou ser um método eficaz, confiável e econômico para monitorar a produção de biossurfactantes. O trabalho mostrou também a capacidade da levedura Candida lipolytica UCP 0988 utilizar óleo de milho e produzir biossurfactantes em água do mar extremamente alcalina (pH inicial 14), suplementada com fontes de nitrogênio e fósforo
Sozgen, Burak. "Neural Network And Regression Models To Decide Whether Or Not To Bid For A Tender In Offshore Petroleum Platform Fabrication Industry." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610820/index.pdf.
Повний текст джерелаRegression Analysis&rdquo
, &ldquo
Neural Network Method&rdquo
and &ldquo
Fuzzy Neural Network Method&rdquo
, are used for modeling of the bidding decision process. The regression analysis examines the data statistically where the neural network method and fuzzy neural network method are based on artificial intelligence. The models are developed using the bidding data compiled from the offshore petroleum platform fabrication projects. In order to compare the prediction performance of these methods &ldquo
Cross Validation Method&rdquo
is utilized. The models developed in this study are compared with the bidding decision method used by the company. The results of the analyses show that regression analysis and neural network method manage to have a prediction performance of 80% and fuzzy neural network has a prediction performance of 77,5% whereas the method used by the company has a prediction performance of 47,5%. The results reveal that the suggested models achieve significant improvement over the existing method for making the correct bidding decision.
Книги з теми "Neural Tensor Network"
Balakrishnan, Kaushik. TensorFlow Reinforcement Learning Quick Start Guide: Get up and Running with Training and Deploying Intelligent, Self-Learning Agents Using Python. Packt Publishing, Limited, 2019.
Знайти повний текст джерелаChurchland, Patricia Smith. Inference to the Best Decision. Edited by John Bickle. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780195304787.003.0017.
Повний текст джерелаHilgurt, S. Ya, and O. A. Chemerys. Reconfigurable signature-based information security tools of computer systems. PH “Akademperiodyka”, 2022. http://dx.doi.org/10.15407/akademperiodyka.458.297.
Повний текст джерелаHupaniittu, Outi, and Ulla-Maija Peltonen, eds. Arkistot ja kulttuuriperintö. SKS Finnish Literature Society, 2021. http://dx.doi.org/10.21435/tl.268.
Повний текст джерелаЧастини книг з теми "Neural Tensor Network"
Huang, Hantao, and Hao Yu. "Tensor-Solver for Deep Neural Network." In Compact and Fast Machine Learning Accelerator for IoT Devices, 63–105. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3323-1_4.
Повний текст джерелаLi, Qiuyue, Nianwen Ning, Bin Wu, and Wenying Guo. "Embedding-Based Network Alignment Using Neural Tensor Networks." In Knowledge Science, Engineering and Management, 401–13. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82153-1_33.
Повний текст джерелаIshibashi, Hideaki, Ryota Shinriki, Hirohisa Isogai, and Tetsuo Furukawa. "Multilevel–Multigroup Analysis Using a Hierarchical Tensor SOM Network." In Neural Information Processing, 459–66. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46675-0_50.
Повний текст джерелаZheng, Yanwei, Yang Zhou, Zengrui Zhao, and Dongxiao Yu. "Adaptive Tensor-Train Decomposition for Neural Network Compression." In Parallel and Distributed Computing, Applications and Technologies, 70–81. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69244-5_6.
Повний текст джерелаGuo, Xiaoyu, Yan Liu, Xianmin Meng, and Lian Liu. "User Identity Linkage Across Social Networks Based on Neural Tensor Network." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 162–71. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66922-5_11.
Повний текст джерелаLi, Wei, and Yunfang Wu. "Hierarchical Gated Recurrent Neural Tensor Network for Answer Triggering." In Lecture Notes in Computer Science, 287–94. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69005-6_24.
Повний текст джерелаWang, Jingchu, Jianyi Liu, Feiyu Chen, Teng Lu, Hua Huang, and Jinmeng Zhao. "Cross-Knowledge Graph Entity Alignment via Neural Tensor Network." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 66–74. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_8.
Повний текст джерелаBai, Yalong, Jianlong Fu, Tiejun Zhao, and Tao Mei. "Deep Attention Neural Tensor Network for Visual Question Answering." In Computer Vision – ECCV 2018, 21–37. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01258-8_2.
Повний текст джерелаWang, Qi, Xiaohong Xiang, and Jun Zhao. "ML-TFN: Multi Layers Tensor Fusion Network for Affective Video Content Analysis." In Neural Computing for Advanced Applications, 184–96. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6142-7_14.
Повний текст джерелаYang, Yongxin, and Timothy M. Hospedales. "Unifying Multi-domain Multitask Learning: Tensor and Neural Network Perspectives." In Domain Adaptation in Computer Vision Applications, 291–309. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58347-1_16.
Повний текст джерелаТези доповідей конференцій з теми "Neural Tensor Network"
Kasiviswanathan, Shiva Prasad, Nina Narodytska, and Hongxia Jin. "Network Approximation using Tensor Sketching." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/321.
Повний текст джерелаChen, Huiyuan, and Jing Li. "Learning Data-Driven Drug-Target-Disease Interaction via Neural Tensor Network." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/477.
Повний текст джерелаTjandra, Andros, Sakriani Sakti, Ruli Manurung, Mirna Adriani, and Satoshi Nakamura. "Gated Recurrent Neural Tensor Network." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727233.
Повний текст джерелаJankovic, Marko, Masashi Sugiyama, and Branimir Reljin. "Tensor based image segmentation." In 2008 9th Symposium on Neural Network Applications in Electrical Engineering. IEEE, 2008. http://dx.doi.org/10.1109/neurel.2008.4685595.
Повний текст джерелаJankovic, Marko V., and Branimir Reljin. "Nonnegative contraction/averaging tensor factorization." In 2010 10th Symposium on Neural Network Applications in Electrical Engineering (NEUREL 2010). IEEE, 2010. http://dx.doi.org/10.1109/neurel.2010.5644083.
Повний текст джерелаPalzer, David, and Brian Hutchinson. "The Tensor Deep Stacking Network Toolkit." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280297.
Повний текст джерелаTjandra, Andros, Sakriani Sakti, and Satoshi Nakamura. "Compressing recurrent neural network with tensor train." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966420.
Повний текст джерелаTjandra, Andros, Sakriani Sakti, and Satoshi Nakamura. "Tensor Decomposition for Compressing Recurrent Neural Network." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489213.
Повний текст джерелаXie, Kun, Huali Lu, Xin Wang, Gaogang Xie, Yong Ding, Dongliang Xie, Jigang Wen, and Dafang Zhang. "Neural Tensor Completion for Accurate Network Monitoring." In IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. IEEE, 2020. http://dx.doi.org/10.1109/infocom41043.2020.9155366.
Повний текст джерелаHe, Xingwei, Hua Xu, Xiaomin Sun, Junhui Deng, and Jia Li. "ABiRCNN with neural tensor network for answer selection." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966171.
Повний текст джерела