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Artykuły w czasopismach na temat "Neural network subspace"
Oja, Erkki. "NEURAL NETWORKS, PRINCIPAL COMPONENTS, AND SUBSPACES". International Journal of Neural Systems 01, nr 01 (styczeń 1989): 61–68. http://dx.doi.org/10.1142/s0129065789000475.
Pełny tekst źródłaEdraki, Marzieh, Nazanin Rahnavard i Mubarak Shah. "SubSpace Capsule Network". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 07 (3.04.2020): 10745–53. http://dx.doi.org/10.1609/aaai.v34i07.6703.
Pełny tekst źródłaZhi, Chuan, Ling Hua Guo, Mei Yun Zhang i Yi Shi. "Research on Dynamic Subspace Divided BP Neural Network Identification Method of Color Space Transform Model". Advanced Materials Research 174 (grudzień 2010): 97–100. http://dx.doi.org/10.4028/www.scientific.net/amr.174.97.
Pełny tekst źródłaFunabashi, Masatoshi. "Synthetic Modeling of Autonomous Learning with a Chaotic Neural Network". International Journal of Bifurcation and Chaos 25, nr 04 (kwiecień 2015): 1550054. http://dx.doi.org/10.1142/s0218127415500546.
Pełny tekst źródłaMahomud, V. A., A. S. Hadi, N. K. Wafi i S. M. R. Taha. "DIRECTION OF ARRIVAL USING PCA NEURALNETWORKS". Journal of Engineering 10, nr 1 (13.03.2024): 83–89. http://dx.doi.org/10.31026/j.eng.2004.01.07.
Pełny tekst źródłaMenghi, Nicholas, Kemal Kacar i Will Penny. "Multitask learning over shared subspaces". PLOS Computational Biology 17, nr 7 (6.07.2021): e1009092. http://dx.doi.org/10.1371/journal.pcbi.1009092.
Pełny tekst źródłaCao, Xiang, i A.-long Yu. "Multi-AUV Cooperative Target Search Algorithm in 3-D Underwater Workspace". Journal of Navigation 70, nr 6 (30.06.2017): 1293–311. http://dx.doi.org/10.1017/s0373463317000376.
Pełny tekst źródłaLaaksonen, Jorma, i Erkki Oja. "Learning Subspace Classifiers and Error-Corrective Feature Extraction". International Journal of Pattern Recognition and Artificial Intelligence 12, nr 04 (czerwiec 1998): 423–36. http://dx.doi.org/10.1142/s0218001498000270.
Pełny tekst źródłaChandar, Sarath, Mitesh M. Khapra, Hugo Larochelle i Balaraman Ravindran. "Correlational Neural Networks". Neural Computation 28, nr 2 (luty 2016): 257–85. http://dx.doi.org/10.1162/neco_a_00801.
Pełny tekst źródłaKizaric, Ben, i Daniel Pimentel-Alarcón. "Principle Component Trees and Their Persistent Homology". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 12 (24.03.2024): 13220–29. http://dx.doi.org/10.1609/aaai.v38i12.29222.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaThis 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.
Pełny tekst źródłaTese (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
Książki na temat "Neural network subspace"
Zhang, Yi, i Zhou Jiliu, red. Subspace learning of neural networks. Boca Raton: CRC Press, 2011.
Znajdź pełny tekst źródłaYi, Zhang, Jian Cheng Lv i Jiliu Zhou. Subspace Learning of Neural Networks. Taylor & Francis Group, 2018.
Znajdź pełny tekst źródłaYi, Zhang, Jian Cheng Lv i Jiliu Zhou. Subspace Learning of Neural Networks. Taylor & Francis Group, 2017.
Znajdź pełny tekst źródłaYi, Zhang, Jian Cheng Lv i Jiliu Zhou. Subspace Learning of Neural Networks. Taylor & Francis Group, 2018.
Znajdź pełny tekst źródłaYi, Zhang, Jian Cheng Lv i Jiliu Zhou. Subspace Learning of Neural Networks. Taylor & Francis Group, 2018.
Znajdź pełny tekst źródłaYi, Zhang, Jian Cheng Lv i Jiliu Zhou. Subspace Learning of Neural Networks. Taylor & Francis Group, 2018.
Znajdź pełny tekst źródłaCzęści książek na temat "Neural network subspace"
Han, Min, i Meiling Xu. "Subspace Echo State Network for Multivariate Time Series Prediction". W Neural Information Processing, 681–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34500-5_80.
Pełny tekst źródłaHu, Yafeng, Feng Zhu i Xianda Zhang. "A Novel Approach for License Plate Recognition Using Subspace Projection and Probabilistic Neural Network". W Advances in Neural Networks – ISNN 2005, 216–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11427445_34.
Pełny tekst źródłaRosso, Marco M., Angelo Aloisio, Raffaele Cucuzza, Dag P. Pasca, Giansalvo Cirrincione i Giuseppe C. Marano. "Structural Health Monitoring with Artificial Neural Network and Subspace-Based Damage Indicators". W Lecture Notes in Civil Engineering, 524–37. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20241-4_37.
Pełny tekst źródłaSmart, Michael H. W. "Rotation invariant IR object recognition using adaptive kernel subspace projections with a neural network". W Biological and Artificial Computation: From Neuroscience to Technology, 1028–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0032562.
Pełny tekst źródłaHafiz Ahamed, Md, i Md Ali Hossain. "Spatial-Spectral Kernel Convolutional Neural Network-Based Subspace Detection for the Task of Hyperspectral Image Classification". W 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.
Pełny tekst źródłaLaaksonen, Jorma, i Erkki Oja. "Subspace dimension selection and averaged learning subspace method in handwritten digit classification". W Artificial Neural Networks — ICANN 96, 227–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61510-5_41.
Pełny tekst źródłaLuo, Fa-Long, i Rolf Unbehauen. "Unsupervised learning of the minor subspace". W Artificial Neural Networks — ICANN 96, 489–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61510-5_84.
Pełny tekst źródłaDi Giacomo, M., i G. Martinelli. "Signal classification by subspace neural networks". W Neural Nets WIRN Vietri-99, 200–205. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0877-1_20.
Pełny tekst źródłaPizarro, Pablo, i Miguel Figueroa. "Subspace-Based Face Recognition on an FPGA". W 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.
Pełny tekst źródłaTeixeira, Ana R., Ana Maria Tomé i E. W. Lang. "Feature Extraction Using Linear and Non-linear Subspace Techniques". W 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.
Pełny tekst źródłaStreszczenia konferencji na temat "Neural network subspace"
Yongqiang Ye i Danwei Wang. "Neural-Network Static Learning Controller in DCT Subspace". W 4th International Conference on Control and Automation. Final Program and Book of Abstracts. IEEE, 2003. http://dx.doi.org/10.1109/icca.2003.1595067.
Pełny tekst źródłaSaeed, Kashif, Nazih Mechbal, Gerard Coffignal i Michel Verge. "Subspace-based damage localization using Artificial Neural Network". W Automation (MED 2010). IEEE, 2010. http://dx.doi.org/10.1109/med.2010.5547729.
Pełny tekst źródłaHeeyoul Choi i Seungjin Choi. "Relative Gradient Learning for Independent Subspace Analysis". W The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.246890.
Pełny tekst źródła"NEURAL NETWORK BASED HAMMERSTEIN SYSTEM IDENTIFICATION USING PARTICLE SWARM SUBSPACE ALGORITHM". W International Conference on Neural Computation. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0003072401820189.
Pełny tekst źródłaChen, Shuyu, Wei Li, Jun Liu, Haoyu Jin i Xuehui Yin. "Network Intrusion Detection Based on Subspace Clustering and BP Neural Network". W 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.
Pełny tekst źródłaYongbo Zhang, Yanping Li i Huakui Wang. "Bilinear Neural Network Tracking Subspace for Blind Multiuser Detection". W 2006 6th World Congress on Intelligent Control and Automation. IEEE, 2006. http://dx.doi.org/10.1109/wcica.2006.1713322.
Pełny tekst źródłaSellar, R., i S. Batill. "Concurrent Subspace Optimization using gradient-enhanced neural network approximations". W 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.
Pełny tekst źródłaGuojun Gan, Jianhong Wu i Zijiang Yang. "PARTCAT: A Subspace Clustering Algorithm for High Dimensional Categorical Data". W The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.247041.
Pełny tekst źródłaJiang-wei, Ge, Zhao Yong-jun i Wang Feng. "A Neural Network Approach for Subspace Decomposition and Its Dimension Estimation". W 2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application. PACIIA 2008. IEEE, 2008. http://dx.doi.org/10.1109/paciia.2008.109.
Pełny tekst źródłaSamarakoon, Lahiru, i Khe Chai Sim. "Subspace LHUC for Fast Adaptation of Deep Neural Network Acoustic Models". W Interspeech 2016. ISCA, 2016. http://dx.doi.org/10.21437/interspeech.2016-1249.
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