Literatura científica selecionada sobre o tema "Functional Pruning"
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Artigos de revistas sobre o assunto "Functional Pruning"
SHAMIR, N., D. SAAD e E. MAROM. "NEURAL NET PRUNING BASED ON FUNCTIONAL BEHAVIOR OF NEURONS". International Journal of Neural Systems 04, n.º 02 (junho de 1993): 143–58. http://dx.doi.org/10.1142/s0129065793000134.
Texto completo da fonteElston, G. N., T. Oga e I. Fujita. "Spinogenesis and Pruning Scales across Functional Hierarchies". Journal of Neuroscience 29, n.º 10 (11 de março de 2009): 3271–75. http://dx.doi.org/10.1523/jneurosci.5216-08.2009.
Texto completo da fonteIwasaki, Hideya, Takeshi Morimoto e Yasunao Takano. "Pruning with improving sequences in lazy functional programs". Higher-Order and Symbolic Computation 24, n.º 4 (novembro de 2011): 281–309. http://dx.doi.org/10.1007/s10990-012-9086-3.
Texto completo da fonteLugaresi, Adriana, Cristiano André Steffens, Angélica Schmitz Heinzen, Cristhian Leonardo Fenili, Alberto Fontanella Brighenti, Mariuccia Schlichting De Martin e Cassandro Vidal Talamini do Amarante. "The influence of the summer pruning on ‘Fuji’ apples storage under controlled atmosphere". Acta Scientiarum. Agronomy 46, n.º 1 (12 de dezembro de 2023): e63557. http://dx.doi.org/10.4025/actasciagron.v46i1.63557.
Texto completo da fonteLi, J., J. Liu, H. Toivonen e J. Yong. "Effective Pruning for the Discovery of Conditional Functional Dependencies". Computer Journal 56, n.º 3 (24 de junho de 2012): 378–92. http://dx.doi.org/10.1093/comjnl/bxs082.
Texto completo da fonteZhang, Qi, Ying Zhang, Pengyao Miao, Meihui Chen, Mengru Du, Xiaomin Pang, Jianghua Ye, Haibin Wang e Xiaoli Jia. "Effects of Pruning on Tea Tree Growth, Soil Enzyme Activity and Microbial Diversity". Agronomy 13, n.º 5 (25 de abril de 2023): 1214. http://dx.doi.org/10.3390/agronomy13051214.
Texto completo da fonteLow, Lawrence K., e Hwai-Jong Cheng. "Axon pruning: an essential step underlying the developmental plasticity of neuronal connections". Philosophical Transactions of the Royal Society B: Biological Sciences 361, n.º 1473 (28 de julho de 2006): 1531–44. http://dx.doi.org/10.1098/rstb.2006.1883.
Texto completo da fonteLeporini, Mariarosaria, Rosa Tundis, Vincenzo Sicari e Monica Rosa Loizzo. "Citrus species: Modern functional food and nutraceutical-based product ingredient". Italian Journal of Food Science 33, n.º 2 (27 de maio de 2021): 63–107. http://dx.doi.org/10.15586/ijfs.v33i2.2009.
Texto completo da fonteMäkelä, Annikki. "A Carbon Balance Model of Growth and Self-Pruning in Trees Based on Structural Relationships". Forest Science 43, n.º 1 (1 de fevereiro de 1997): 7–24. http://dx.doi.org/10.1093/forestscience/43.1.7.
Texto completo da fonteSun, Xiaochuan, Yu Wang, Mingxiang Hao, Yingqi Li e Tianyu Huang. "Reservoir structure optimization of echo state networks: A detrended multiple cross-correlation pruning perspective". Journal of Intelligent & Fuzzy Systems 46, n.º 5-6 (24 de outubro de 2024): 11263–75. http://dx.doi.org/10.3233/jifs-233605.
Texto completo da fonteTeses / dissertações sobre o assunto "Functional Pruning"
Shabarshova, Liudmila. "Geometric functional pruning for change point detection in low-dimensional exponential family models". Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASM026.
Texto completo da fonteChange point detection is a common unsupervised learning problem in many application areas, especially in biology, genomics, sensor network monitoring, and cyber-security. Typically, either a posteriori change detection, i.e. offline, or sequential change detection, i.e. online, is considered.Standard dynamic programming methods for change point detection have been proposed to optimise either the likelihood or the log-likelihood ratio of a change point model. These methods are exact and recover optimal segmentations. However, they have quadratic complexity. Continuously reducing the set of potential change point candidates, called pruning, is a way to reduce the computational complexity of standard dynamic programming methods. Over the last decade, a new class of dynamic programming methods, called functional pruning, has been proposed. The functional pruning techniques used in these methods have already proved to be computationally efficient for univariate parametric change point models. Extending univariate functional pruning rules to multivariate settings is difficult if we aim for the most efficient pruning. It leads to non-convex optimisation problems.This thesis introduces two novel, computationally efficient, functional pruning dynamic programming methods for the detection of change points in low-dimensional exponential family models: the offline multiple change point detection method, GeomFPOP (Kmax = ∞), and the online single change point detection method, MdFOCuS.Computational geometry is the basis of the functional pruning rules for these methods. The pruning rule of GeomFPOP (Kmax = ∞) uses a geometric heuristic to update and prune potential change point candidates over time. The pruning rule of MdFOCuS uses a connection to a convex hull problem that simplifies the search for change point location to be pruned. Further we mathematically demonstrate that this pruning technique leads to a quasi-linear runtime complexity.These two pruning rules show significant improvements in computational complexity for low-dimensional exponential family models in simulation studies. In one minute, the Rcpp implementations of these methods can process more than 2 × 106 observations in a bivariate signal without change with i.i.d. Gaussian noise
Prewitt, Sarah F. "Phylogenetic and Functional Characterization of Cotton (Gossypium hirsutum) CENTRORADIALIS/TERMINAL FLOWER1/SELF-PRUNING Genes". Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc1062895/.
Texto completo da fontePalm, Emanuel. "Implications and Impact of Blockchain Transaction Pruning". Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-64986.
Texto completo da fonteKučírek, Tomáš. "Umělá inteligence pro hraní her". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-412861.
Texto completo da fonteKubisz, Jan. "Využití umělé inteligence k monitorování stavu obráběcího stroje". Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-417752.
Texto completo da fonteCapítulos de livros sobre o assunto "Functional Pruning"
Qiu, Shoumeng, Yuzhang Gu e Xiaolin Zhang. "BFRIFP: Brain Functional Reorganization Inspired Filter Pruning". In Lecture Notes in Computer Science, 16–28. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86380-7_2.
Texto completo da fonteQiu, Shoumeng, Yuzhang Gu e Xiaolin Zhang. "Correction to: BFRIFP: Brain Functional Reorganization Inspired Filter Pruning". In Lecture Notes in Computer Science, C1. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86380-7_57.
Texto completo da fonteZouggar, Souad Taleb, e Abdelkader Adla. "A New Function for Ensemble Pruning". In Decision Support Systems VIII: Sustainable Data-Driven and Evidence-Based Decision Support, 181–90. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90315-6_15.
Texto completo da fonteSha, Chaofeng, Keqiang Wang, Xiaoling Wang e Aoying Zhou. "Ensemble Pruning: A Submodular Function Maximization Perspective". In Database Systems for Advanced Applications, 1–15. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05813-9_1.
Texto completo da fonteShi, Daming, Junbin Gao, Daniel So Yeung e Fei Chen. "Radial Basis Function Network Pruning by Sensitivity Analysis". In Advances in Artificial Intelligence, 380–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24840-8_27.
Texto completo da fonteSutton-Charani, Nicolas, Sébastien Destercke e Thierry Denœux. "Training and Evaluating Classifiers from Evidential Data: Application to E2M Decision Tree Pruning". In Belief Functions: Theory and Applications, 87–94. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11191-9_10.
Texto completo da fonteBorera, Eddy C., Larry D. Pyeatt, Arisoa S. Randrianasolo e Madhi Naser-Moghadasi. "POMDP Filter: Pruning POMDP Value Functions with the Kaczmarz Iterative Method". In Advances in Artificial Intelligence, 254–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16761-4_23.
Texto completo da fonteHoque, Md Tamjidul, Madhu Chetty e Laurence S. Dooley. "Efficient Computation of Fitness Function by Pruning in Hydrophobic-Hydrophilic Model". In Biological and Medical Data Analysis, 346–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11573067_35.
Texto completo da fonteParkinson, Randall W., Monica Perez-Bedmar e Jenna A. Santangelo. "Red mangrove (Rhizophora mangle L.) litter fall response to selective pruning (Indian River Lagoon, Florida, U.S.A.)". In Diversity and Function in Mangrove Ecosystems, 63–76. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4078-2_7.
Texto completo da fonteLi, Jing, Bao-Liang Lu e Michinori Ichikawa. "An Algorithm for Pruning Redundant Modules in Min-Max Modular Network with GZC Function". In Lecture Notes in Computer Science, 293–302. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539087_35.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Functional Pruning"
Yang, Dun-An, Jing-Jia Liou e Harry H. Chen. "Transient Fault Pruning for Effective Candidate Reduction in Functional Debugging". In 2022 IEEE International Test Conference (ITC). IEEE, 2022. http://dx.doi.org/10.1109/itc50671.2022.00014.
Texto completo da fonteLi, Qingwei, Wenyong Zhang, Zhiwen Shen e He Qifeng. "Photovoltaic power output forecasting based on similar day analysis and sensitive pruning extreme learning machine". In 2022 International Conference on Optoelectronic Information and Functional Materials (OIFM 2022), editado por Chao Zuo. SPIE, 2022. http://dx.doi.org/10.1117/12.2638674.
Texto completo da fonteCheng, Feng, e Zhe Yang. "New Pruning Methods for Mining Minimal Functional Dependencies from Large-Scale Distributed Data". In 2018 Sixth International Conference on Advanced Cloud and Big Data (CBD). IEEE, 2018. http://dx.doi.org/10.1109/cbd.2018.00055.
Texto completo da fonteMandros, Panagiotis, Mario Boley e Jilles Vreeken. "Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms". In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/864.
Texto completo da fonteGhosh, Gourhari, Ajay Sidpara e P. P. Bandyopadhyay. "Characterization of Nanofinished WC-Co Coating Using Advanced 3D Surface Texture Parameters". In ASME 2018 13th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/msec2018-6592.
Texto completo da fonteLiu, Yuchen, S. Y. Kung e David Wentzlaff. "Evolving transferable neural pruning functions". In GECCO '22: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3512290.3528694.
Texto completo da fonteRodriguez Lizana, Antonio, Maria Joao Pereira, Alzira Ramos, Manuel Moreno Garcia e Manuel Ribeiro. "STUDY OF THE UNCERTAINTY OF THE AMOUNT OF PRUNING IN THE OLIVE GROVE USING GEOSTATISTICAL ALGORITHMS". In 22nd International Multidisciplinary Scientific GeoConference 2022. STEF92 Technology, 2022. http://dx.doi.org/10.5593/sgem2022v/3.2/s14.50.
Texto completo da fonteOliveira, Saulo A. F., Ajalmar R. Rocha Neto e João P. P. Gomes. "On Model Complexity Reduction in Instance-Based Learners". In Anais Estendidos da Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/sibgrapi.est.2022.23253.
Texto completo da fonteFlávia de A. Campos, Lívia, Liara M. de Mattos, Aline D. P. dos Santos e Luis C. Paschoarelli. "An Approach to Evaluation of Aesthetic Function on Usability: An Exploratory Study About Descriptors of Aesthetic in Pruning Shears". In Applied Human Factors and Ergonomics Conference. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001315.
Texto completo da fonteSetiono, R., e A. Gaweda. "Neural network pruning for function approximation". In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. IEEE, 2000. http://dx.doi.org/10.1109/ijcnn.2000.859435.
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