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Auswahl der wissenschaftlichen Literatur zum Thema „Functional Pruning“
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Zeitschriftenartikel zum Thema "Functional Pruning"
SHAMIR, N., D. SAAD und E. MAROM. „NEURAL NET PRUNING BASED ON FUNCTIONAL BEHAVIOR OF NEURONS“. International Journal of Neural Systems 04, Nr. 02 (Juni 1993): 143–58. http://dx.doi.org/10.1142/s0129065793000134.
Der volle Inhalt der QuelleElston, G. N., T. Oga und I. Fujita. „Spinogenesis and Pruning Scales across Functional Hierarchies“. Journal of Neuroscience 29, Nr. 10 (11.03.2009): 3271–75. http://dx.doi.org/10.1523/jneurosci.5216-08.2009.
Der volle Inhalt der QuelleIwasaki, Hideya, Takeshi Morimoto und Yasunao Takano. „Pruning with improving sequences in lazy functional programs“. Higher-Order and Symbolic Computation 24, Nr. 4 (November 2011): 281–309. http://dx.doi.org/10.1007/s10990-012-9086-3.
Der volle Inhalt der QuelleLugaresi, Adriana, Cristiano André Steffens, Angélica Schmitz Heinzen, Cristhian Leonardo Fenili, Alberto Fontanella Brighenti, Mariuccia Schlichting De Martin und Cassandro Vidal Talamini do Amarante. „The influence of the summer pruning on ‘Fuji’ apples storage under controlled atmosphere“. Acta Scientiarum. Agronomy 46, Nr. 1 (12.12.2023): e63557. http://dx.doi.org/10.4025/actasciagron.v46i1.63557.
Der volle Inhalt der QuelleLi, J., J. Liu, H. Toivonen und J. Yong. „Effective Pruning for the Discovery of Conditional Functional Dependencies“. Computer Journal 56, Nr. 3 (24.06.2012): 378–92. http://dx.doi.org/10.1093/comjnl/bxs082.
Der volle Inhalt der QuelleZhang, Qi, Ying Zhang, Pengyao Miao, Meihui Chen, Mengru Du, Xiaomin Pang, Jianghua Ye, Haibin Wang und Xiaoli Jia. „Effects of Pruning on Tea Tree Growth, Soil Enzyme Activity and Microbial Diversity“. Agronomy 13, Nr. 5 (25.04.2023): 1214. http://dx.doi.org/10.3390/agronomy13051214.
Der volle Inhalt der QuelleLow, Lawrence K., und 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, Nr. 1473 (28.07.2006): 1531–44. http://dx.doi.org/10.1098/rstb.2006.1883.
Der volle Inhalt der QuelleLeporini, Mariarosaria, Rosa Tundis, Vincenzo Sicari und Monica Rosa Loizzo. „Citrus species: Modern functional food and nutraceutical-based product ingredient“. Italian Journal of Food Science 33, Nr. 2 (27.05.2021): 63–107. http://dx.doi.org/10.15586/ijfs.v33i2.2009.
Der volle Inhalt der QuelleMäkelä, Annikki. „A Carbon Balance Model of Growth and Self-Pruning in Trees Based on Structural Relationships“. Forest Science 43, Nr. 1 (01.02.1997): 7–24. http://dx.doi.org/10.1093/forestscience/43.1.7.
Der volle Inhalt der QuelleSun, Xiaochuan, Yu Wang, Mingxiang Hao, Yingqi Li und Tianyu Huang. „Reservoir structure optimization of echo state networks: A detrended multiple cross-correlation pruning perspective“. Journal of Intelligent & Fuzzy Systems 46, Nr. 5-6 (24.10.2024): 11263–75. http://dx.doi.org/10.3233/jifs-233605.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleChange 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/.
Der volle Inhalt der QuellePalm, 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.
Der volle Inhalt der QuelleKučírek, 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.
Der volle Inhalt der QuelleKubisz, 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.
Der volle Inhalt der QuelleBuchteile zum Thema "Functional Pruning"
Qiu, Shoumeng, Yuzhang Gu und 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.
Der volle Inhalt der QuelleQiu, Shoumeng, Yuzhang Gu und 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.
Der volle Inhalt der QuelleZouggar, Souad Taleb, und 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.
Der volle Inhalt der QuelleSha, Chaofeng, Keqiang Wang, Xiaoling Wang und 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.
Der volle Inhalt der QuelleShi, Daming, Junbin Gao, Daniel So Yeung und 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.
Der volle Inhalt der QuelleSutton-Charani, Nicolas, Sébastien Destercke und 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.
Der volle Inhalt der QuelleBorera, Eddy C., Larry D. Pyeatt, Arisoa S. Randrianasolo und 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.
Der volle Inhalt der QuelleHoque, Md Tamjidul, Madhu Chetty und 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.
Der volle Inhalt der QuelleParkinson, Randall W., Monica Perez-Bedmar und 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.
Der volle Inhalt der QuelleLi, Jing, Bao-Liang Lu und 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Functional Pruning"
Yang, Dun-An, Jing-Jia Liou und 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.
Der volle Inhalt der QuelleLi, Qingwei, Wenyong Zhang, Zhiwen Shen und 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), herausgegeben von Chao Zuo. SPIE, 2022. http://dx.doi.org/10.1117/12.2638674.
Der volle Inhalt der QuelleCheng, Feng, und 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.
Der volle Inhalt der QuelleMandros, Panagiotis, Mario Boley und 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.
Der volle Inhalt der QuelleGhosh, Gourhari, Ajay Sidpara und 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.
Der volle Inhalt der QuelleLiu, Yuchen, S. Y. Kung und 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.
Der volle Inhalt der QuelleRodriguez Lizana, Antonio, Maria Joao Pereira, Alzira Ramos, Manuel Moreno Garcia und 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.
Der volle Inhalt der QuelleOliveira, Saulo A. F., Ajalmar R. Rocha Neto und 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.
Der volle Inhalt der QuelleFlávia de A. Campos, Lívia, Liara M. de Mattos, Aline D. P. dos Santos und 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.
Der volle Inhalt der QuelleSetiono, R., und 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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