Littérature scientifique sur le sujet « Neural network subspace »
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Articles de revues sur le sujet "Neural network subspace"
Oja, Erkki. « NEURAL NETWORKS, PRINCIPAL COMPONENTS, AND SUBSPACES ». International Journal of Neural Systems 01, no 01 (janvier 1989) : 61–68. http://dx.doi.org/10.1142/s0129065789000475.
Texte intégralEdraki, Marzieh, Nazanin Rahnavard et Mubarak Shah. « SubSpace Capsule Network ». Proceedings of the AAAI Conference on Artificial Intelligence 34, no 07 (3 avril 2020) : 10745–53. http://dx.doi.org/10.1609/aaai.v34i07.6703.
Texte intégralZhi, Chuan, Ling Hua Guo, Mei Yun Zhang et Yi Shi. « Research on Dynamic Subspace Divided BP Neural Network Identification Method of Color Space Transform Model ». Advanced Materials Research 174 (décembre 2010) : 97–100. http://dx.doi.org/10.4028/www.scientific.net/amr.174.97.
Texte intégralFunabashi, Masatoshi. « Synthetic Modeling of Autonomous Learning with a Chaotic Neural Network ». International Journal of Bifurcation and Chaos 25, no 04 (avril 2015) : 1550054. http://dx.doi.org/10.1142/s0218127415500546.
Texte intégralMahomud, V. A., A. S. Hadi, N. K. Wafi et S. M. R. Taha. « DIRECTION OF ARRIVAL USING PCA NEURALNETWORKS ». Journal of Engineering 10, no 1 (13 mars 2024) : 83–89. http://dx.doi.org/10.31026/j.eng.2004.01.07.
Texte intégralMenghi, Nicholas, Kemal Kacar et Will Penny. « Multitask learning over shared subspaces ». PLOS Computational Biology 17, no 7 (6 juillet 2021) : e1009092. http://dx.doi.org/10.1371/journal.pcbi.1009092.
Texte intégralCao, Xiang, et A.-long Yu. « Multi-AUV Cooperative Target Search Algorithm in 3-D Underwater Workspace ». Journal of Navigation 70, no 6 (30 juin 2017) : 1293–311. http://dx.doi.org/10.1017/s0373463317000376.
Texte intégralLaaksonen, Jorma, et Erkki Oja. « Learning Subspace Classifiers and Error-Corrective Feature Extraction ». International Journal of Pattern Recognition and Artificial Intelligence 12, no 04 (juin 1998) : 423–36. http://dx.doi.org/10.1142/s0218001498000270.
Texte intégralChandar, Sarath, Mitesh M. Khapra, Hugo Larochelle et Balaraman Ravindran. « Correlational Neural Networks ». Neural Computation 28, no 2 (février 2016) : 257–85. http://dx.doi.org/10.1162/neco_a_00801.
Texte intégralKizaric, Ben, et Daniel Pimentel-Alarcón. « Principle Component Trees and Their Persistent Homology ». Proceedings of the AAAI Conference on Artificial Intelligence 38, no 12 (24 mars 2024) : 13220–29. http://dx.doi.org/10.1609/aaai.v38i12.29222.
Texte intégralThèses sur le sujet "Neural network subspace"
Gaya, Jean-Baptiste. « Subspaces of Policies for Deep Reinforcement Learning ». Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS075.
Texte intégralThis work explores "Subspaces of Policies for Deep Reinforcement Learning," introducing an innovative approach to address adaptability and generalization challenges in deep reinforcement learning (RL). Situated within the broader context of the AI revolution, this research emphasizes the shift toward scalable and generalizable models in RL, inspired by advancements in deep learning architectures and methodologies. It identifies the limitations of current RL applications, particularly in achieving generalization across varied tasks and domains, proposing a paradigm shift towards adaptive methods.The research initially tackles zero-shot generalization, assessing deep RL's maturity in generalizing across unseen tasks without additional training. Through investigations into morphological generalization and multi-objective reinforcement learning (MORL), critical limitations in current methods are identified, and novel approaches to improve generalization capabilities are introduced. Notably, work on weight averaging in MORL presents a straightforward method for optimizing multiple objectives, showing promise for future exploration.The core contribution lies in developing a "Subspace of Policies" framework. This novel approach advocates for maintaining a dynamic landscape of solutions within a smaller parametric space, taking profit of neural network weight averaging. Functional diversity is achieved with minimal computational overhead through weight interpolation between neural network parameters. This methodology is explored through various experiments and settings, including few-shot adaptation and continual reinforcement learning, demonstrating its efficacy and potential for scalability and adaptability in complex RL tasks.The conclusion reflects on the research journey, emphasizing the implications of the "Subspaces of Policies" framework for future AI research. Several future directions are outlined, including enhancing the scalability of subspace methods, exploring their potential in decentralized settings, and addressing challenges in efficiency and interpretability. This foundational contribution to the field of RL paves the way for innovative solutions to long-standing challenges in adaptability and generalization, marking a significant step forward in the development of autonomous agents capable of navigating a wide array of tasks seamlessly
Del, Real Tamariz Annabell. « Modelagem computacional de dados e controle inteligente no espaço de estado ». [s.n.], 2005. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260207.
Texte intégralTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
Made available in DSpace on 2018-08-04T18:33:31Z (GMT). No. of bitstreams: 1 DelRealTamariz_Annabell_D.pdf: 5783881 bytes, checksum: 21a1a2e27552398a982a934513988a24 (MD5) Previous issue date: 2005
Resumo: Este estudo apresenta contribuições para modelagem computacional de dados multivariáveis no espaço de estado, tanto com sistemas lineares invariantes como com variantes no tempo. Propomos para modelagem determinística-estocástica de dados ruidosos, o Algoritmo MOESP_AOKI. Propomos, utilizando Redes Neurais Recorrentes multicamadas, algoritmos para resolver a Equação Algébrica de Riccati Discreta bem como a Inequação Algébrica de Riccati Discreta, via Desigualdades Matriciais Lineares. Propomos um esquema de controle adaptativo com Escalonamento de Ganhos, baseado em Redes Neurais, para sistemas multivariáveis discretos variantes no tempo, identificados pelo algoritmo MOESP_VAR, também proposto nesta tese. Em síntese, uma estrutura de controle inteligente para sistemas discretos multivariáveis variantes no tempo, através de uma abordagem que pode ser chamada ILPV (Intelligent Linear Parameter Varying), é proposta e implementada. Um controlador LPV Inteligente, para dados computacionalmente modelados pelo algoritmo MOESP_VAR, é concretizado, implementado e testado com bons resultados
Abstract: This study presents contributions for state space multivariable computational data modelling with discrete time invariant as well as with time varying linear systems. A proposal for Deterministic-Estocastica Modelling of noisy data, MOESP_AOKI Algorithm, is made. We present proposals forsolving the Discrete-Time Algebraic Riccati Equation as well as the associate Linear Matrix Inequalityusing a multilayer Recurrent Neural Network approaches. An Intelligent Linear Parameter Varying(ILPV) control approach for multivariable discrete Linear Time Varying (LTV) systems identified bythe MOESP_VAR algorithm, are both proposed. A gain scheduling adaptive control scheme based on neural networks is designed to tune on-line the optimal controllers. In synthesis, an Intelligent Linear Parameter Varying (ILPV) Control approach for multivariable discrete Linear Time Varying Systems (LTV), identified by the algorithm MOESP_VAR, is proposed. This way an Intelligent LPV Control for multivariable data computationally modeled via the MOESP_VAR algorithm is structured, implemented and tested with good results
Doutorado
Automação
Doutor em Engenharia Elétrica
Livres sur le sujet "Neural network subspace"
Zhang, Yi, et Zhou Jiliu, dir. Subspace learning of neural networks. Boca Raton : CRC Press, 2011.
Trouver le texte intégralYi, Zhang, Jian Cheng Lv et Jiliu Zhou. Subspace Learning of Neural Networks. Taylor & Francis Group, 2018.
Trouver le texte intégralYi, Zhang, Jian Cheng Lv et Jiliu Zhou. Subspace Learning of Neural Networks. Taylor & Francis Group, 2017.
Trouver le texte intégralYi, Zhang, Jian Cheng Lv et Jiliu Zhou. Subspace Learning of Neural Networks. Taylor & Francis Group, 2018.
Trouver le texte intégralYi, Zhang, Jian Cheng Lv et Jiliu Zhou. Subspace Learning of Neural Networks. Taylor & Francis Group, 2018.
Trouver le texte intégralYi, Zhang, Jian Cheng Lv et Jiliu Zhou. Subspace Learning of Neural Networks. Taylor & Francis Group, 2018.
Trouver le texte intégralChapitres de livres sur le sujet "Neural network subspace"
Han, Min, et Meiling Xu. « Subspace Echo State Network for Multivariate Time Series Prediction ». Dans Neural Information Processing, 681–88. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34500-5_80.
Texte intégralHu, Yafeng, Feng Zhu et Xianda Zhang. « A Novel Approach for License Plate Recognition Using Subspace Projection and Probabilistic Neural Network ». Dans Advances in Neural Networks – ISNN 2005, 216–21. Berlin, Heidelberg : Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11427445_34.
Texte intégralRosso, Marco M., Angelo Aloisio, Raffaele Cucuzza, Dag P. Pasca, Giansalvo Cirrincione et Giuseppe C. Marano. « Structural Health Monitoring with Artificial Neural Network and Subspace-Based Damage Indicators ». Dans Lecture Notes in Civil Engineering, 524–37. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20241-4_37.
Texte intégralSmart, Michael H. W. « Rotation invariant IR object recognition using adaptive kernel subspace projections with a neural network ». Dans Biological and Artificial Computation : From Neuroscience to Technology, 1028–37. Berlin, Heidelberg : Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0032562.
Texte intégralHafiz Ahamed, Md, et Md Ali Hossain. « Spatial-Spectral Kernel Convolutional Neural Network-Based Subspace Detection for the Task of Hyperspectral Image Classification ». Dans Proceedings of International Conference on Information and Communication Technology for Development, 163–70. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7528-8_13.
Texte intégralLaaksonen, Jorma, et Erkki Oja. « Subspace dimension selection and averaged learning subspace method in handwritten digit classification ». Dans Artificial Neural Networks — ICANN 96, 227–32. Berlin, Heidelberg : Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61510-5_41.
Texte intégralLuo, Fa-Long, et Rolf Unbehauen. « Unsupervised learning of the minor subspace ». Dans Artificial Neural Networks — ICANN 96, 489–94. Berlin, Heidelberg : Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61510-5_84.
Texte intégralDi Giacomo, M., et G. Martinelli. « Signal classification by subspace neural networks ». Dans Neural Nets WIRN Vietri-99, 200–205. London : Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0877-1_20.
Texte intégralPizarro, Pablo, et Miguel Figueroa. « Subspace-Based Face Recognition on an FPGA ». Dans Engineering Applications of Neural Networks, 84–89. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23957-1_10.
Texte intégralTeixeira, Ana R., Ana Maria Tomé et E. W. Lang. « Feature Extraction Using Linear and Non-linear Subspace Techniques ». Dans Artificial Neural Networks – ICANN 2009, 115–24. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04277-5_12.
Texte intégralActes de conférences sur le sujet "Neural network subspace"
Yongqiang Ye et Danwei Wang. « Neural-Network Static Learning Controller in DCT Subspace ». Dans 4th International Conference on Control and Automation. Final Program and Book of Abstracts. IEEE, 2003. http://dx.doi.org/10.1109/icca.2003.1595067.
Texte intégralSaeed, Kashif, Nazih Mechbal, Gerard Coffignal et Michel Verge. « Subspace-based damage localization using Artificial Neural Network ». Dans Automation (MED 2010). IEEE, 2010. http://dx.doi.org/10.1109/med.2010.5547729.
Texte intégralHeeyoul Choi et Seungjin Choi. « Relative Gradient Learning for Independent Subspace Analysis ». Dans The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.246890.
Texte intégral« NEURAL NETWORK BASED HAMMERSTEIN SYSTEM IDENTIFICATION USING PARTICLE SWARM SUBSPACE ALGORITHM ». Dans International Conference on Neural Computation. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0003072401820189.
Texte intégralChen, Shuyu, Wei Li, Jun Liu, Haoyu Jin et Xuehui Yin. « Network Intrusion Detection Based on Subspace Clustering and BP Neural Network ». Dans 2021 8th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2021 7th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). IEEE, 2021. http://dx.doi.org/10.1109/cscloud-edgecom52276.2021.00022.
Texte intégralYongbo Zhang, Yanping Li et Huakui Wang. « Bilinear Neural Network Tracking Subspace for Blind Multiuser Detection ». Dans 2006 6th World Congress on Intelligent Control and Automation. IEEE, 2006. http://dx.doi.org/10.1109/wcica.2006.1713322.
Texte intégralSellar, R., et S. Batill. « Concurrent Subspace Optimization using gradient-enhanced neural network approximations ». Dans 6th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina : American Institute of Aeronautics and Astronautics, 1996. http://dx.doi.org/10.2514/6.1996-4019.
Texte intégralGuojun Gan, Jianhong Wu et Zijiang Yang. « PARTCAT : A Subspace Clustering Algorithm for High Dimensional Categorical Data ». Dans The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.247041.
Texte intégralJiang-wei, Ge, Zhao Yong-jun et Wang Feng. « A Neural Network Approach for Subspace Decomposition and Its Dimension Estimation ». Dans 2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application. PACIIA 2008. IEEE, 2008. http://dx.doi.org/10.1109/paciia.2008.109.
Texte intégralSamarakoon, Lahiru, et Khe Chai Sim. « Subspace LHUC for Fast Adaptation of Deep Neural Network Acoustic Models ». Dans Interspeech 2016. ISCA, 2016. http://dx.doi.org/10.21437/interspeech.2016-1249.
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