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Artykuły w czasopismach na temat "Embedding space"
Takehara, Daisuke, i Kei Kobayashi. "Representing Hierarchical Structured Data Using Cone Embedding". Mathematics 11, nr 10 (15.05.2023): 2294. http://dx.doi.org/10.3390/math11102294.
Pełny tekst źródłaSamko, Natasha. "Embeddings of weighted generalized Morrey spaces into Lebesgue spaces on fractal sets". Fractional Calculus and Applied Analysis 22, nr 5 (25.10.2019): 1203–24. http://dx.doi.org/10.1515/fca-2019-0064.
Pełny tekst źródłaPaston, Sergey, i Taisiia Zaitseva. "Nontrivial Isometric Embeddings for Flat Spaces". Universe 7, nr 12 (4.12.2021): 477. http://dx.doi.org/10.3390/universe7120477.
Pełny tekst źródłaRavindran, Renjith P., i Kavi Narayana Murthy. "Syntactic Coherence in Word Embedding Spaces". International Journal of Semantic Computing 15, nr 02 (czerwiec 2021): 263–90. http://dx.doi.org/10.1142/s1793351x21500057.
Pełny tekst źródłaLi, Pandeng, Yan Li, Hongtao Xie i Lei Zhang. "Neighborhood-Adaptive Structure Augmented Metric Learning". Proceedings of the AAAI Conference on Artificial Intelligence 36, nr 2 (28.06.2022): 1367–75. http://dx.doi.org/10.1609/aaai.v36i2.20025.
Pełny tekst źródłaBhowmik, Kowshik, i Anca Ralescu. "Clustering of Monolingual Embedding Spaces". Digital 3, nr 1 (23.02.2023): 48–66. http://dx.doi.org/10.3390/digital3010004.
Pełny tekst źródłaHawley, Scott H., Zach Evans i Joe Baldridge. "Audio (vector) algebra: Vector space operations on neural audio embeddings". Journal of the Acoustical Society of America 152, nr 4 (październik 2022): A178. http://dx.doi.org/10.1121/10.0015957.
Pełny tekst źródłaHashimoto, Tatsunori B., David Alvarez-Melis i Tommi S. Jaakkola. "Word Embeddings as Metric Recovery in Semantic Spaces". Transactions of the Association for Computational Linguistics 4 (grudzień 2016): 273–86. http://dx.doi.org/10.1162/tacl_a_00098.
Pełny tekst źródłaMarinari, Maria Grazia, i Mario Raimondo. "On Complete Intersections Over an Algebraically Non-Closed Field". Canadian Mathematical Bulletin 29, nr 2 (1.06.1986): 140–45. http://dx.doi.org/10.4153/cmb-1986-024-0.
Pełny tekst źródłaMinemyer, Barry. "Isometric embeddings of polyhedra into Euclidean space". Journal of Topology and Analysis 07, nr 04 (22.09.2015): 677–92. http://dx.doi.org/10.1142/s179352531550020x.
Pełny tekst źródłaRozprawy doktorskie na temat "Embedding space"
Zhang, Xinhua, i xinhua zhang cs@gmail com. "Graphical Models: Modeling, Optimization, and Hilbert Space Embedding". The Australian National University. ANU College of Engineering and Computer Sciences, 2010. http://thesis.anu.edu.au./public/adt-ANU20100729.072500.
Pełny tekst źródłaGibert, Domingo Jaume. "Vector Space Embedding of Graphs via Statistics of Labelling Information". Doctoral thesis, Universitat Autònoma de Barcelona, 2012. http://hdl.handle.net/10803/96240.
Pełny tekst źródłaPattern recognition is the task that aims at distinguishing objects among different classes. When such a task wants to be solved in an automatic way a crucial step is how to formally represent such patterns to the computer. Based on the different representational formalisms, we may distinguish between statistical and structural pattern recognition. The former describes objects as a set of measurements arranged in the form of what is called a feature vector. The latter assumes that relations between parts of the underlying objects need to be explicitly represented and thus it uses relational structures such as graphs for encoding their inherent information. Vector spaces are a very flexible mathematical structure that has allowed to come up with several efficient ways for the analysis of patterns under the form of feature vectors. Nevertheless, such a representation cannot explicitly cope with binary relations between parts of the objects and it is restricted to measure the exact same number of features for each pattern under study regardless of their complexity. Graph-based representations present the contrary situation. They can easily adapt to the inherent complexity of the patterns but introduce a problem of high computational complexity, hindering the design of efficient tools to process and analyze patterns. Solving this paradox is the main goal of this thesis. The ideal situation for solving pattern recognition problems would be to represent the patterns using relational structures such as graphs, and to be able to use the wealthy repository of data processing tools from the statistical pattern recognition domain. An elegant solution to this problem is to transform the graph domain into a vector domain where any processing algorithm can be applied. In other words, by mapping each graph to a point in a vector space we automatically get access to the rich set of algorithms from the statistical domain to be applied in the graph domain. Such methodology is called graph embedding. In this thesis we propose to associate feature vectors to graphs in a simple and very efficient way by just putting attention on the labelling information that graphs store. In particular, we count frequencies of node labels and of edges between labels. Although their locality, these features are able to robustly represent structurally global properties of graphs, when considered together in the form of a vector. We initially deal with the case of discrete attributed graphs, where features are easy to compute. The continuous case is tackled as a natural generalization of the discrete one, where rather than counting node and edge labelling instances, we count statistics of some representatives of them. We encounter how the proposed vectorial representations of graphs suffer from high dimensionality and correlation among components and we face these problems by feature selection algorithms. We also explore how the diversity of different embedding representations can be exploited in order to boost the performance of base classifiers in a multiple classifier systems framework. An extensive experimental evaluation finally shows how the methodology we propose can be efficiently computed and compete with other graph matching and embedding methodologies.
Sandvick, Joshua Sandvick. "Machine Translation Through the Creation of a Common Embedding Space". The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531420294211248.
Pełny tekst źródłaBishop, Jonathan R. B. "Embedding population dynamics in mark-recapture models". Thesis, St Andrews, 2009. http://hdl.handle.net/10023/718.
Pełny tekst źródłaDube, Matthew P. "An Embedding Graph for 9-Intersection Topological Spatial Relations". Fogler Library, University of Maine, 2009. http://www.library.umaine.edu/theses/pdf/DubeMP2009.pdf.
Pełny tekst źródłaDonald, Andrew. "Embedding 3-manifolds in 4-space and link concordance via double branched covers". Thesis, University of Glasgow, 2013. http://theses.gla.ac.uk/4425/.
Pełny tekst źródłaStrickrodt, Marianne [Verfasser], i Tobias [Akademischer Betreuer] Meilinger. "The impossible puzzle : No global embedding in environmental space memory / Marianne Strickrodt ; Betreuer: Tobias Meilinger". Tübingen : Universitätsbibliothek Tübingen, 2019. http://d-nb.info/1190639653/34.
Pełny tekst źródłaStewart, Nigel Timothy, i nigels@nigels com. "An Image-Space Algorithm for Hardware-Based Rendering of Constructive Solid Geometry". RMIT University. Aerospace, Mechanical and Manufacturing Engineering, 2008. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080721.144757.
Pełny tekst źródłaBELLAVITA, CARLO. "FUNCTIONAL PROPERTIES OF P-DE BRANGES SPACES". Doctoral thesis, Università degli Studi di Milano, 2022. http://hdl.handle.net/2434/924712.
Pełny tekst źródłaSinnokrot, Mohanned Omar. "Space-time block codes with low maximum-likelihood decoding complexity". Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31752.
Pełny tekst źródłaCommittee Chair: Barry, John; Committee Co-Chair: Madisetti, Vijay; Committee Member: Andrew, Alfred; Committee Member: Li, Ye; Committee Member: Ma, Xiaoli; Committee Member: Stuber, Gordon. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Książki na temat "Embedding space"
Froehlich, Annette, red. Embedding Space in African Society. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06040-4.
Pełny tekst źródłaRiesen, Kaspar. Graph classification and clustering based on vector space embedding. New Jersey: World Scientific, 2010.
Znajdź pełny tekst źródła1975-, Parcet Javier, red. Mixed-norm inequalities and operator space Lp embedding theory. Providence, R.I: American Mathematical Society, 2010.
Znajdź pełny tekst źródłaTimashev, D. A. Homogeneous Spaces and Equivariant Embeddings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18399-7.
Pełny tekst źródłaservice), SpringerLink (Online, red. Homogeneous Spaces and Equivariant Embeddings. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.
Znajdź pełny tekst źródłaEdmunds, David E., i W. Desmond Evans. Hardy Operators, Function Spaces and Embeddings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-07731-3.
Pełny tekst źródłaEdmunds, David E. Hardy Operators, Function Spaces and Embeddings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.
Znajdź pełny tekst źródła1942-, Hong Jia-Xing, red. Isometric embedding of Riemannian manifolds in Euclidean spaces. Providence, R.I: American Mathematical Society, 2006.
Znajdź pełny tekst źródłaBernard, Maurey, red. H [delta]-embeddings in Hilbert space and optimization on G [delta]-sets. Providence, R.I., USA: American Mathematical Society, 1986.
Znajdź pełny tekst źródłaEnvelopes and sharp embeddings of function spaces. Boca Raton, FL: Chapman & Hall/CRC, 2007.
Znajdź pełny tekst źródłaCzęści książek na temat "Embedding space"
Martens, Bas, Alexander Gairiseb i Carl Eriksen. "Embedding Space in Society". W Southern Space Studies, 335–56. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05980-4_20.
Pełny tekst źródłaVempala, Santosh. "Embedding metrics in Euclidean space". W DIMACS Series in Discrete Mathematics and Theoretical Computer Science, 15–25. Providence, Rhode Island: American Mathematical Society, 2005. http://dx.doi.org/10.1090/dimacs/065/03.
Pełny tekst źródłaUncu, Baran Alp. "Embedding the prefigurations of the Gezi protests". W Public Space Democracy, 47–73. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003193753-5.
Pełny tekst źródłaGallier, Jean. "Embedding an Affine Space in a Vector Space". W Texts in Applied Mathematics, 70–86. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0137-0_4.
Pełny tekst źródłaGallier, Jean. "Embedding an Affine Space in a Vector Space". W Texts in Applied Mathematics, 85–101. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9961-0_4.
Pełny tekst źródłaSiebrits, André, Bas Martens i Carl Eriksen. "Initiatives for Embedding Space Applications in African Societies". W Southern Space Studies, 357–73. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05980-4_21.
Pełny tekst źródłaBunke, Horst, i Kaspar Riesen. "Graph Classification on Dissimilarity Space Embedding". W Lecture Notes in Computer Science, 2. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89689-0_2.
Pełny tekst źródłaSmola, Alex, Arthur Gretton, Le Song i Bernhard Schölkopf. "A Hilbert Space Embedding for Distributions". W Lecture Notes in Computer Science, 13–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75225-7_5.
Pełny tekst źródłaHou, Haiwei, Shifei Ding, Xiao Xu i Lili Guo. "Deep Friendly Embedding Space for Clustering". W IFIP Advances in Information and Communication Technology, 92–105. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57808-3_7.
Pełny tekst źródłaFeintuch, Avraham. "Orthogonal Embedding of Time-Varying Systems". W Robust Control Theory in Hilbert Space, 207–16. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-0591-3_11.
Pełny tekst źródłaStreszczenia konferencji na temat "Embedding space"
Pereira, João, Albert K. Groen, Erik S. G. Stroes i Evgeni Levin. "Graph Space Embedding". W 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.2019/451.
Pełny tekst źródłaZhang, Yizhou, Guojie Song, Lun Du, Shuwen Yang i Yilun Jin. "DANE: Domain Adaptive Network Embedding". W 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.2019/606.
Pełny tekst źródłaDghais, Wael, Luis Nero Alves, Joana Catarina Mendes, Jonathan Rodriguez i Jose Carlos Pedro. "Memristor state-space embedding". W 2015 European Conference on Circuit Theory and Design (ECCTD). IEEE, 2015. http://dx.doi.org/10.1109/ecctd.2015.7300040.
Pełny tekst źródłaIoannou, Yani, Limin Shang, Robin Harrap i Michael Greenspan. "Local PotentialWell Space Embedding". W 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops. IEEE, 2009. http://dx.doi.org/10.1109/iccvw.2009.5457491.
Pełny tekst źródłaDing, Chuntao, Li Zhang i Bangjun Wang. "Hidden space discriminant neighborhood embedding". W 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889365.
Pełny tekst źródłaDar, Guy, Mor Geva, Ankit Gupta i Jonathan Berant. "Analyzing Transformers in Embedding Space". W Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.acl-long.893.
Pełny tekst źródłaKo, Byungsoo, i Geonmo Gu. "Embedding Expansion: Augmentation in Embedding Space for Deep Metric Learning". W 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00728.
Pełny tekst źródłaYang, Liang, Yuexue Wang, Junhua Gu, Chuan Wang, Xiaochun Cao i Yuanfang Guo. "JANE: Jointly Adversarial Network Embedding". W 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.2020/192.
Pełny tekst źródłaChen, Ying-Nong, Gang-Feng Ho, Kuo-Chin Fan, Chi-Hung Chuang i Chih-Chang Yu. "Orthogonal Nearest Neighbor Feature Space Embedding". W 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). IEEE, 2012. http://dx.doi.org/10.1109/iih-msp.2012.46.
Pełny tekst źródłaShang, Limin, i Michael Greenspan. "Pose Determination By PotentialWell Space Embedding". W Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007). IEEE, 2007. http://dx.doi.org/10.1109/3dim.2007.40.
Pełny tekst źródłaRaporty organizacyjne na temat "Embedding space"
Holzapfel, Rolf-Peter. Jacobi Theta Embedding of a Hyperbolic 4-Space with Cusps. GIQ, 2012. http://dx.doi.org/10.7546/giq-3-2002-11-63.
Pełny tekst źródłaBano, Masooda, i Zeena Oberoi. Embedding Innovation in State Systems: Lessons from Pratham in India. Research on Improving Systems of Education (RISE), grudzień 2020. http://dx.doi.org/10.35489/bsg-rise-wp_2020/058.
Pełny tekst źródłaMcReynolds, Stephanie JH, Peter Verheyen, Terriruth Carrier i Scott Warren. Library Impact Research Report: Distinct Academic Learning Communities at Syracuse University Libraries. Association of Research Libraries, lipiec 2022. http://dx.doi.org/10.29242/report.syracuse2022.
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