Literatura académica sobre el tema "Decomposition de tenseur"
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Artículos de revistas sobre el tema "Decomposition de tenseur"
Zheng, Yu-Bang, Ting-Zhu Huang, Xi-Le Zhao, Qibin Zhao y Tai-Xiang Jiang. "Fully-Connected Tensor Network Decomposition and Its Application to Higher-Order Tensor Completion". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 12 (18 de mayo de 2021): 11071–78. http://dx.doi.org/10.1609/aaai.v35i12.17321.
Texto completoLuo, Dijun, Chris Ding y Heng Huang. "Multi-Level Cluster Indicator Decompositions of Matrices and Tensors". Proceedings of the AAAI Conference on Artificial Intelligence 25, n.º 1 (4 de agosto de 2011): 423–28. http://dx.doi.org/10.1609/aaai.v25i1.7933.
Texto completoBreiding, Paul y Nick Vannieuwenhoven. "On the average condition number of tensor rank decompositions". IMA Journal of Numerical Analysis 40, n.º 3 (20 de junio de 2019): 1908–36. http://dx.doi.org/10.1093/imanum/drz026.
Texto completoAlhamadi, Khaled M., Fahmi Yaseen Qasem y Meqdad Ahmed Ali. "Different types of decomposition for certain tensors in \(K^h-BR-F_n\) and \(K^h-BR\)-affinely connected space". University of Aden Journal of Natural and Applied Sciences 20, n.º 2 (31 de agosto de 2016): 355–63. http://dx.doi.org/10.47372/uajnas.2016.n2.a10.
Texto completoHameduddin, Ismail, Charles Meneveau, Tamer A. Zaki y Dennice F. Gayme. "Geometric decomposition of the conformation tensor in viscoelastic turbulence". Journal of Fluid Mechanics 842 (12 de marzo de 2018): 395–427. http://dx.doi.org/10.1017/jfm.2018.118.
Texto completoObster, Dennis y Naoki Sasakura. "Counting Tensor Rank Decompositions". Universe 7, n.º 8 (15 de agosto de 2021): 302. http://dx.doi.org/10.3390/universe7080302.
Texto completoAdkins, William A. "Primary decomposition of torsionR[X]-modules". International Journal of Mathematics and Mathematical Sciences 17, n.º 1 (1994): 41–46. http://dx.doi.org/10.1155/s0161171294000074.
Texto completoCai, Yunfeng y Ping Li. "A Blind Block Term Decomposition of High Order Tensors". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 8 (18 de mayo de 2021): 6868–76. http://dx.doi.org/10.1609/aaai.v35i8.16847.
Texto completoHauenstein, Jonathan D., Luke Oeding, Giorgio Ottaviani y Andrew J. Sommese. "Homotopy techniques for tensor decomposition and perfect identifiability". Journal für die reine und angewandte Mathematik (Crelles Journal) 2019, n.º 753 (1 de agosto de 2019): 1–22. http://dx.doi.org/10.1515/crelle-2016-0067.
Texto completoZhu, Ben-Chao y Xiang-Song Chen. "Tensor gauge condition and tensor field decomposition". Modern Physics Letters A 30, n.º 35 (28 de octubre de 2015): 1550192. http://dx.doi.org/10.1142/s0217732315501928.
Texto completoTesis sobre el tema "Decomposition de tenseur"
Harmouch, Jouhayna. "Décomposition de petit rang, problèmes de complétion et applications : décomposition de matrices de Hankel et des tenseurs de rang faible". Thesis, Université Côte d'Azur (ComUE), 2018. http://www.theses.fr/2018AZUR4236/document.
Texto completoWe study the decomposition of a multivariate Hankel matrix as a sum of Hankel matrices of small rank in correlation with the decomposition of its symbol σ as a sum of polynomialexponential series. We present a new algorithm to compute the low rank decomposition of the Hankel operator and the decomposition of its symbol exploiting the properties of the associated Artinian Gorenstein quotient algebra . A basis of is computed from the Singular Value Decomposition of a sub-matrix of the Hankel matrix . The frequencies and the weights are deduced from the generalized eigenvectors of pencils of shifted sub-matrices of Explicit formula for the weights in terms of the eigenvectors avoid us to solve a Vandermonde system. This new method is a multivariate generalization of the so-called Pencil method for solving Pronytype decomposition problems. We analyse its numerical behaviour in the presence of noisy input moments, and describe a rescaling technique which improves the numerical quality of the reconstruction for frequencies of high amplitudes. We also present a new Newton iteration, which converges locally to the closest multivariate Hankel matrix of low rank and show its impact for correcting errors on input moments. We study the decomposition of a multi-symmetric tensor T as a sum of powers of product of linear forms in correlation with the decomposition of its dual as a weighted sum of evaluations. We use the properties of the associated Artinian Gorenstein Algebra to compute the decomposition of its dual which is defined via a formal power series τ. We use the low rank decomposition of the Hankel operator associated to the symbol τ into a sum of indecomposable operators of low rank. A basis of is chosen such that the multiplication by some variables is possible. We compute the sub-coordinates of the evaluation points and their weights using the eigen-structure of multiplication matrices. The new algorithm that we propose works for small rank. We give a theoretical generalized approach of the method in n dimensional space. We show a numerical example of the decomposition of a multi-linear tensor of rank 3 in 3 dimensional space. We show a numerical example of the decomposition of a multi-symmetric tensor of rank 3 in 3 dimensional space. We study the completion problem of the low rank Hankel matrix as a minimization problem. We use the relaxation of it as a minimization problem of the nuclear norm of Hankel matrix. We adapt the SVT algorithm to the case of Hankel matrix and we compute the linear operator which describes the constraints of the problem and its adjoint. We try to show the utility of the decomposition algorithm in some applications such that the LDA model and the ODF model
Blanc, Katy. "Description de contenu vidéo : mouvements et élasticité temporelle". Thesis, Université Côte d'Azur (ComUE), 2018. http://www.theses.fr/2018AZUR4212/document.
Texto completoVideo recognition gain in performance during the last years, especially due to the improvement in the deep learning performances on images. However the jump in recognition rate on images does not directly impact the recognition rate on videos. This limitation is certainly due to this added dimension, the time, on which a robust description is still hard to extract. The recurrent neural networks introduce temporality but they have a limited memory. State of the art methods for video description usually handle time as a spatial dimension and the combination of video description methods reach the current best accuracies. However the temporal dimension has its own elasticity, different from the spatial dimensions. Indeed, the temporal dimension of a video can be locally deformed: a partial dilatation produces a visual slow down during the video, without changing the understanding, in contrast with a spatial dilatation on an image which will modify the proportions of the shown objects. We can thus expect to improve the video content classification by creating an invariant description to these speed changes. This thesis focus on the question of a robust video description considering the elasticity of the temporal dimension under three different angles. First, we have locally and explicitly described the motion content. Singularities are detected in the optical flow, then tracked along the time axis and organized in chain to describe video part. We have used this description on sport content. Then we have extracted global and implicit description thanks to tensor decompositions. Tensor enables to consider a video as a multi-dimensional data table. The extracted description are evaluated in a classification task. Finally, we have studied speed normalization method thanks to Dynamical Time Warping methods on series. We have showed that this normalization improve the classification rates
André, Rémi. "Algorithmes de diagonalisation conjointe par similitude pour la décomposition canonique polyadique de tenseurs : applications en séparation de sources". Thesis, Toulon, 2018. http://www.theses.fr/2018TOUL0011/document.
Texto completoThis thesis introduces new joint eigenvalue decomposition algorithms. These algorithms allowamongst others to solve the canonical polyadic decomposition problem. This decomposition iswidely used for blind source separation. Using the joint eigenvalue decomposition to solve thecanonical polyadic decomposition problem allows to avoid some problems whose the others canonicalpolyadic decomposition algorithms generally suffer, such as the convergence rate, theoverfactoring sensibility and the correlated factors sensibility. The joint eigenvalue decompositionalgorithms dealing with complex data give either good results when the noise power is low, orthey are robust to the noise power but have a high numerical cost. Therefore, we first proposealgorithms equally dealing with real and complex. Moreover, in some applications, factor matricesof the canonical polyadic decomposition contain only nonnegative values. Taking this constraintinto account makes the algorithms more robust to the overfactoring and to the correlated factors.Therefore, we also offer joint eigenvalue decomposition algorithms taking advantage of thisnonnegativity constraint. Suggested numerical simulations show that the first developed algorithmsimprove the estimation accuracy and reduce the numerical cost in the case of complexdata. Our numerical simulations also highlight the fact that our nonnegative joint eigenvaluedecomposition algorithms improve the factor matrices estimation when their columns have ahigh correlation degree. Eventually, we successfully applied our algorithms to two blind sourceseparation problems : one concerning numerical telecommunications and the other concerningfluorescence spectroscopy
Nguyen, Dinh Quoc Dang. "Representation of few-group homogenized cross sections by polynomials and tensor decomposition". Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASP142.
Texto completoThis thesis focuses on studying the mathematical modeling of few-group homogenized cross sections, a critical element in the two-step scheme widely used in nuclear reactor simulations. As industrial demands increasingly require finer spatial and energy meshes to improve the accuracy of core calculations, the size of the cross section library can become excessive, hampering the performance of core calculations. Therefore, it is essential to develop a representation that minimizes memory usage while still enabling efficient data interpolation.Two approaches, polynomial representation and Canonical Polyadic decomposition of tensors, are presented and applied to few-group homogenized cross section data. The data is prepared using APOLLO3 on the geometry of two assemblies in the X2 VVER-1000 benchmark. The compression rate and accuracy are evaluated and discussed for each approach to determine their applicability to the standard two-step scheme.Additionally, GPU implementations of both approaches are tested to assess the scalability of the algorithms based on the number of threads involved. These implementations are encapsulated in a library called Merlin, intended for future research and industrial applications that involve these approaches.Both approaches, particularly the method of tensor decomposition, demonstrate promising results in terms of data compression and reconstruction accuracy. Integrating these methods into the standard two-step scheme would not only substantially reduce memory usage for storing cross sections, but also significantly decrease the computational effort required for interpolating cross sections during core calculations, thereby reducing overall calculation time for industrial reactor simulations
Akhavanbahabadi, Saeed. "Analyse des Crises d’Epilepsie à l’Aide de Mesures de Profondeur". Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAT063.
Texto completoAbsence epilepsy syndrome is accompanied with sudden appearance of seizures in different regions of the brain. The sudden generalization of absence seizures to every region of the brain shows the existence of a mechanism which can quickly synchronizes the activities of the majority of neurons in the brain. The presence of such a mechanism challenges our information about the integrative properties of neurons and the functional connectivity of brain networks. For this reason, many researchers have tried to recognize the main origin of absence seizures. Recent studies have suggested a theory regarding the origin of absence seizures which states that somatosensory cortex drives the thalamus during the first cycles of absence seizures, while thereafter, cortex and thalamus mutually drive each other and continue absence seizures.This theory motivated the neuroscientists in Grenoble Institute of Neurosciences (GIN) to record data from different layers of somatosensory cortex of Genetic Absence Epilepsy Rats from Strasbourg (GAERS), which is a well-validate animal model for absence epilepsy, to explore the main starting region of absence seizures locally. An electrode with E = 16 sensors was vertically implanted in somatosensory cortex of GAERS, and potentials were recorded. In this study, we aim to localize the onset layers of somatosensory cortex during absence seizures and investigate the temporal evolution and dynamics of absence seizures using the recorded data. It is worth mentioning that all previous studies have investigated absence seizures using the data recorded from different regions of the brain, while this is the first study that performs the local exploration of absence seizures using the data recorded from different layers of somatosensory cortex, i.e., the main starting region of absence seizures.Using factor analysis, source separation, and blind deconvolution methods in different scenarios, we show that 1) the top and bottom layers of somatosensory cortex activate more than the other layers during absence seizures, 2) there is a background epileptic activity during absence seizures, 3) there are few activities or states which randomly activate with the background epileptic activity to generate the absence seizures, and 4) one of these states is dominant, and the others are unstable
André, Rémi. "Algorithmes de diagonalisation conjointe par similitude pour la décomposition canonique polyadique de tenseurs : applications en séparation de sources". Electronic Thesis or Diss., Toulon, 2018. http://www.theses.fr/2018TOUL0011.
Texto completoThis thesis introduces new joint eigenvalue decomposition algorithms. These algorithms allowamongst others to solve the canonical polyadic decomposition problem. This decomposition iswidely used for blind source separation. Using the joint eigenvalue decomposition to solve thecanonical polyadic decomposition problem allows to avoid some problems whose the others canonicalpolyadic decomposition algorithms generally suffer, such as the convergence rate, theoverfactoring sensibility and the correlated factors sensibility. The joint eigenvalue decompositionalgorithms dealing with complex data give either good results when the noise power is low, orthey are robust to the noise power but have a high numerical cost. Therefore, we first proposealgorithms equally dealing with real and complex. Moreover, in some applications, factor matricesof the canonical polyadic decomposition contain only nonnegative values. Taking this constraintinto account makes the algorithms more robust to the overfactoring and to the correlated factors.Therefore, we also offer joint eigenvalue decomposition algorithms taking advantage of thisnonnegativity constraint. Suggested numerical simulations show that the first developed algorithmsimprove the estimation accuracy and reduce the numerical cost in the case of complexdata. Our numerical simulations also highlight the fact that our nonnegative joint eigenvaluedecomposition algorithms improve the factor matrices estimation when their columns have ahigh correlation degree. Eventually, we successfully applied our algorithms to two blind sourceseparation problems : one concerning numerical telecommunications and the other concerningfluorescence spectroscopy
Silva, Alex Pereira da. "Techniques tensorielles pour le traitement du signal : algorithmes pour la décomposition polyadique canonique". Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT042/document.
Texto completoLow rank tensor decomposition has been playing for the last years an important rolein many applications such as blind source separation, telecommunications, sensor array processing,neuroscience, chemometrics, and data mining. The Canonical Polyadic tensor decomposition is veryattractive when compared to standard matrix-based tools, manly on system identification. In this thesis,we propose: (i) several algorithms to compute specific low rank-approximations: finite/iterativerank-1 approximations, iterative deflation approximations, and orthogonal tensor decompositions. (ii)A new strategy to solve multivariate quadratic systems, where this problem is reduced to a best rank-1 tensor approximation problem. (iii) Theoretical results to study and proof the performance or theconvergence of some algorithms. All performances are supported by numerical experiments
A aproximação tensorial de baixo posto desempenha nestes últimos anos um papel importanteem várias aplicações, tais como separação cega de fontes, telecomunicações, processamentode antenas, neurociênca, quimiometria e exploração de dados. A decomposição tensorial canônicaé bastante atrativa se comparada às técnicas matriciais clássicas, principalmente na identificação desistemas. Nesta tese, propõe-se (i) vários algoritmos para calcular alguns tipos de aproximação deposto: aproximação de posto-1 iterativa e em um número finito de operações, a aproximação pordeflação iterativa, e a decomposição tensorial ortogonal; (ii) uma nova estratégia para resolver sistemasquadráticos em várias variáveis, em que tal problema pode ser reduzido à melhor aproximaçãode posto-1 de um tensor; (iii) resultados teóricos visando estudar o desempenho ou demonstrar aconvergência de alguns algoritmos. Todas os desempenhos são ilustrados através de simulações computacionais
Marmin, Arthur. "Rational models optimized exactly for solving signal processing problems". Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG017.
Texto completoA wide class of nonconvex optimization problem is represented by rational optimization problems. The latter appear naturally in many areas such as signal processing or chemical engineering. However, finding the global optima of such problems is intricate. A recent approach called Lasserre's hierarchy provides a sequence of convex problems that has the theoretical guarantee to converge to the global optima. Nevertheless, this approach is computationally challenging due to the high dimensions of the convex relaxations. In this thesis, we tackle this challenge for various signal processing problems.First, we formulate the reconstruction of sparse signals as a rational optimization problem. We show that the latter has a structure that we wan exploit in order to reduce the complexity of the associated relaxations. We thus solve several practical problems such as the reconstruction of chromatography signals. We also extend our method to the reconstruction of various types of signal corrupted by different noise models.In a second part, we study the convex relaxations generated by our problems which take the form of high-dimensional semi-definite programming problems. We consider several algorithms mainly based on proximal operators to solve those high-dimensional problems efficiently.The last part of this thesis is dedicated to the link between polynomial optimization and symmetric tensor decomposition. Indeed, they both can be seen as an instance of the moment problem. We thereby propose a detection method as well as a decomposition algorithm for symmetric tensors based on the tools used in polynomial optimization. In parallel, we suggest a robust extraction method for polynomial optimization based on tensor decomposition algorithms. Those methods are illustrated on signal processing problems
Brandoni, Domitilla. "Tensor decompositions for Face Recognition". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16867/.
Texto completoBender, Matias Rafael. "Algorithms for sparse polynomial systems : Gröbner bases and resultants". Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS029.
Texto completoSolving polynomial systems is one of the oldest and most important problems in computational mathematics and has many applications in several domains of science and engineering. It is an intrinsically hard problem with complexity at least single exponential in the number of variables. However, in most of the cases, the polynomial systems coming from applications have some kind of structure. In this thesis we focus on exploiting the structure related to the sparsity of the supports of the polynomials; that is, we exploit the fact that the polynomials only have a few monomials with non-zero coefficients. Our objective is to solve the systems faster than the worst case estimates that assume that all the terms are present. We say that a sparse system is unmixed if all its polynomials have the same Newton polytope, and mixed otherwise. Most of the work on solving sparse systems concern the unmixed case, with the exceptions of mixed sparse resultants and homotopy methods. In this thesis, we develop algorithms for mixed systems. We use two prominent tools in nonlinear algebra: sparse resultants and Groebner bases. We work on each theory independently, but we also combine them to introduce new algorithms: we take advantage of the algebraic properties of the systems associated to a non-vanishing resultant to improve the complexity of computing their Groebner bases; for example, we exploit the exactness of some strands of the associated Koszul complex to deduce an early stopping criterion for our Groebner bases algorithms and to avoid every redundant computation (reductions to zero). In addition, we introduce quasi-optimal algorithms to decompose binary forms
Libros sobre el tema "Decomposition de tenseur"
Cheng, Lei, Zhongtao Chen y Yik-Chung Wu. Bayesian Tensor Decomposition for Signal Processing and Machine Learning. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22438-6.
Texto completoTheocaris, Pericles S. On a general theory of anisotropy of matter: The spectral decomposition of the compliance tensor : application to crystallography. Athēnai: Grapheion Dēmosieumatōn tēs Akadēmias Athēnōn, 1999.
Buscar texto completoKondrat'ev, Gennadiy. Clifford Geometric Algebra. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1832489.
Texto completoNinul, Anatolij Sergeevič. Tensor Trigonometry. Moscow, Russia: Fizmatlit Publisher, 2021.
Buscar texto completoNinul, Anatolij Sergeevič. Tenzornaja trigonometrija: Teorija i prilozenija / Theory and Applications /. Moscow, Russia: Mir Publisher, 2004.
Buscar texto completoStructured Tensor Recovery and Decomposition. [New York, N.Y.?]: [publisher not identified], 2017.
Buscar texto completoMaggiore, Michele. Helicity decomposition of metric perturbations. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198570899.003.0009.
Texto completoFavier, Gérard. Matrix and Tensor Decompositions in Signal Processing. Wiley & Sons, Incorporated, John, 2021.
Buscar texto completoFavier, Gérard. Matrix and Tensor Decompositions in Signal Processing. Wiley & Sons, Incorporated, John, 2021.
Buscar texto completoFavier, Gérard. Matrix and Tensor Decompositions in Signal Processing. Wiley & Sons, Incorporated, John, 2021.
Buscar texto completoCapítulos de libros sobre el tema "Decomposition de tenseur"
Pajarola, Renato, Susanne K. Suter, Rafael Ballester-Ripoll y Haiyan Yang. "Tensor Approximation for Multidimensional and Multivariate Data". En Mathematics and Visualization, 73–98. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56215-1_4.
Texto completoLandsberg, J. "Tensor decomposition". En Graduate Studies in Mathematics, 289–310. Providence, Rhode Island: American Mathematical Society, 2011. http://dx.doi.org/10.1090/gsm/128/12.
Texto completoTaguchi, Y.-h. "Tensor Decomposition". En Unsupervised and Semi-Supervised Learning, 47–78. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22456-1_3.
Texto completoLiu, Yipeng, Jiani Liu, Zhen Long y Ce Zhu. "Tensor Decomposition". En Tensor Computation for Data Analysis, 19–57. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74386-4_2.
Texto completoTaguchi, Y.-h. "Tensor Decomposition". En Unsupervised and Semi-Supervised Learning, 47–77. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-60982-4_3.
Texto completoHao, N., L. Horesh y M. E. Kilmer. "Nonnegative Tensor Decomposition". En Compressed Sensing & Sparse Filtering, 123–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38398-4_5.
Texto completoTaguchi, Y. H. "Tensor Decomposition in Genomics". En Machine Learning and IoT Applications for Health Informatics, 131–43. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003424987-7.
Texto completoZhu, Hu, Yushan Pan, Lizhen Deng y Guoxia Xu. "Low-Rank Tensor Decomposition". En Infrared Small Target Detection, 41–82. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9799-2_4.
Texto completoHarada, Kenji, Hiroaki Matsueda y Tsuyoshi Okubo. "Application of Tensor Network Formalism for Processing Tensor Data". En Advanced Mathematical Science for Mobility Society, 79–100. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9772-5_5.
Texto completoDevarajan, Karthik. "Matrix and Tensor Decompositions". En Problem Solving Handbook in Computational Biology and Bioinformatics, 291–318. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-0-387-09760-2_14.
Texto completoActas de conferencias sobre el tema "Decomposition de tenseur"
Joshi, Spriha, Philippe Dreesen, Pietro Bonizzi, Joël Karel, Ralf Peeters y Martijn Boussé. "Novel Tensor-based Singular Spectrum Decomposition". En 2024 32nd European Signal Processing Conference (EUSIPCO), 1327–31. IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715185.
Texto completoMatveev, Sergey Alexandrovich y Aleksandr A. Kurilovich. "Utilization of Tensor Decompositions for Video-compression". En 33rd International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2023. http://dx.doi.org/10.20948/graphicon-2023-582-589.
Texto completoAlexeev, Alexey Kirillovich, Alexander Evgenyevich Bondarev y Yu S. Pyatakova. "On the Visualization of the Ensemble of Parametric Numerical Solutions Using Tensor Decomposition". En 33rd International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2023. http://dx.doi.org/10.20948/graphicon-2023-292-301.
Texto completoCao, Tianxiao, Lu Sun, Canh Hao Nguyen y Hiroshi Mamitsuka. "Learning Low-Rank Tensor Cores with Probabilistic ℓ0-Regularized Rank Selection for Model Compression". En Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/418.
Texto completoOzdemir, Alp, Mark A. Iwen y Selin Aviyente. "Multiscale tensor decomposition". En 2016 50th Asilomar Conference on Signals, Systems and Computers. IEEE, 2016. http://dx.doi.org/10.1109/acssc.2016.7869118.
Texto completoKumar, Shrawan. "Tensor Product Decomposition". En Proceedings of the International Congress of Mathematicians 2010 (ICM 2010). Published by Hindustan Book Agency (HBA), India. WSPC Distribute for All Markets Except in India, 2011. http://dx.doi.org/10.1142/9789814324359_0094.
Texto completoGhavam, Kamyar y Reza Naghdabadi. "Corotational Analysis of Elastic-Plastic Hardening Materials Based on Different Kinematic Decompositions". En ASME 2006 Pressure Vessels and Piping/ICPVT-11 Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/pvp2006-icpvt-11-93442.
Texto completoZHANG, Jianfu, ZERUI TAO, LIQING ZHANG y QIBIN ZHAO. "Tensor Decomposition Via Core Tensor Networks". En ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9413637.
Texto completoHinrich, Jesper L. y Morten Morup. "Probabilistic Tensor Train Decomposition". En 2019 27th European Signal Processing Conference (EUSIPCO). IEEE, 2019. http://dx.doi.org/10.23919/eusipco.2019.8903177.
Texto completoDu, Yishuai, Yimin Zheng, Kuang-chih Lee y Shandian Zhe. "Probabilistic Streaming Tensor Decomposition". En 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 2018. http://dx.doi.org/10.1109/icdm.2018.00025.
Texto completoInformes sobre el tema "Decomposition de tenseur"
Tamara G. Kolda. Orthogonal tensor decompositions. Office of Scientific and Technical Information (OSTI), marzo de 2000. http://dx.doi.org/10.2172/755101.
Texto completoDevine, Karen y Grey Ballard. GentenMPI: Distributed Memory Sparse Tensor Decomposition. Office of Scientific and Technical Information (OSTI), agosto de 2020. http://dx.doi.org/10.2172/1656940.
Texto completoPhipps, Eric, Nick Johnson y Tamara Kolda. STREAMING GENERALIZED CANONICAL POLYADIC TENSOR DECOMPOSITIONS. Office of Scientific and Technical Information (OSTI), octubre de 2021. http://dx.doi.org/10.2172/1832304.
Texto completoAnandkumar, Anima, Rong Ge, Daniel Hsu, Sham M. Kakade y Matus Telgarsky. Tensor Decompositions for Learning Latent Variable Models. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 2012. http://dx.doi.org/10.21236/ada604494.
Texto completoLopez, Oscar, Richard Lehoucq y Daniel Dunlavy. Zero-Truncated Poisson Tensor Decomposition for Sparse Count Data. Office of Scientific and Technical Information (OSTI), enero de 2022. http://dx.doi.org/10.2172/1841834.
Texto completoDunlavy, Daniel M., Evrim Acar y Tamara Gibson Kolda. An optimization approach for fitting canonical tensor decompositions. Office of Scientific and Technical Information (OSTI), febrero de 2009. http://dx.doi.org/10.2172/978916.
Texto completoRuiz, Trevor y Charlotte R. Ellison. Spatiotemporally coherent tensor decompositions for the analysis of trajectory data. Engineer Research and Development Center (U.S.), julio de 2020. http://dx.doi.org/10.21079/11681/37355.
Texto completoMyers, Jeremy y Daniel Dunlavy. Tensor Decompositions for Count Data that Leverage Stochastic and Deterministic Optimization. Office of Scientific and Technical Information (OSTI), agosto de 2023. http://dx.doi.org/10.2172/2430475.
Texto completoAnandkumar, Animashree, Daniel Hsu, Majid Janzamin y Sham Kakade. When are Overcomplete Representations Identifiable? Uniqueness of Tensor Decompositions Under Expansion Constraints. Fort Belvoir, VA: Defense Technical Information Center, junio de 2013. http://dx.doi.org/10.21236/ada604842.
Texto completoMyers, Jeremy y Daniel Dunlavy. A Hybrid Method for Tensor Decompositions that Leverages Stochastic and Deterministic Optimization. Office of Scientific and Technical Information (OSTI), abril de 2022. http://dx.doi.org/10.2172/1865529.
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