Bibliografias temáticas / Butterfly factorization

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Literatura científica selecionada sobre o tema "Butterfly factorization"

Autor: Grafiati
Publicado: 31 de agosto de 2024

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Artigos de revistas sobre o assunto "Butterfly factorization"

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Li, Yingzhou, Haizhao Yang, Eileen R. Martin, Kenneth L. Ho, and Lexing Ying. "Butterfly Factorization." Multiscale Modeling & Simulation 13, no. 2 (2015): 714–32. http://dx.doi.org/10.1137/15m1007173.

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Li, Yingzhou, and Haizhao Yang. "Interpolative Butterfly Factorization." SIAM Journal on Scientific Computing 39, no. 2 (2017): A503—A531. http://dx.doi.org/10.1137/16m1074941.

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Li, Yingzhou, Haizhao Yang, and Lexing Ying. "Multidimensional butterfly factorization." Applied and Computational Harmonic Analysis 44, no. 3 (2018): 737–58. http://dx.doi.org/10.1016/j.acha.2017.04.002.

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Pang, Qiyuan, Kenneth L. Ho, and Haizhao Yang. "Interpolative Decomposition Butterfly Factorization." SIAM Journal on Scientific Computing 42, no. 2 (2020): A1097—A1115. http://dx.doi.org/10.1137/19m1294873.

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Liu, Yang, Xin Xing, Han Guo, Eric Michielssen, Pieter Ghysels, and Xiaoye Sherry Li. "Butterfly Factorization Via Randomized Matrix-Vector Multiplications." SIAM Journal on Scientific Computing 43, no. 2 (2021): A883—A907. http://dx.doi.org/10.1137/20m1315853.

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Chen, Ze, Juan Zhang, Kenneth L. Ho, and Haizhao Yang. "Multidimensional phase recovery and interpolative decomposition butterfly factorization." Journal of Computational Physics 412 (July 2020): 109427. http://dx.doi.org/10.1016/j.jcp.2020.109427.

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Jaber, Marwan A., та Daniel Massicotte. "Radix-2α/4β Building Blocks for Efficient VLSI’s Higher Radices Butterflies Implementation". VLSI Design 2014 (13 травня 2014): 1–13. http://dx.doi.org/10.1155/2014/690594.

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This paper describes an embedded FFT processor where the higher radices butterflies maintain one complex multiplier in its critical path. Based on the concept of a radix-r fast Fourier factorization and based on the FFT parallel processing, we introduce a new concept of a radix-r Fast Fourier Transform in which the concept of the radix-r butterfly computation has been formulated as the combination of radix-2α/4β butterflies implemented in parallel. By doing so, the VLSI butterfly implementation for higher radices would be feasible since it maintains approximately the same complexity of the rad
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Bremer, James, Ze Chen, and Haizhao Yang. "Rapid Application of the Spherical Harmonic Transform via Interpolative Decomposition Butterfly Factorization." SIAM Journal on Scientific Computing 43, no. 6 (2021): A3789—A3808. http://dx.doi.org/10.1137/20m1333845.

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Yang, Haizhao. "A unified framework for oscillatory integral transforms: When to use NUFFT or butterfly factorization?" Journal of Computational Physics 388 (July 2019): 103–22. http://dx.doi.org/10.1016/j.jcp.2019.02.044.

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Mardan, Suha Suliman, and Mounir Taha Hamood. "New fast Walsh–Hadamard–Hartley transform algorithm." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 1533. http://dx.doi.org/10.11591/ijece.v13i2.pp1533-1540.

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<span lang="EN-US">This paper presents an efficient fast Walsh–Hadamard–Hartley transform (FWHT) algorithm that incorporates the computation of the Walsh-Hadamard transform (WHT) with the discrete Hartley transform (DHT) into an orthogonal, unitary single fast transform possesses the block diagonal structure. The proposed algorithm is implemented in an integrated butterfly structure utilizing the sparse matrices factorization approach and the Kronecker (tensor) product technique, which proved a valuable and fast tool for developing and analyzing the proposed algorithm. The proposed appro
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Teses / dissertações sobre o assunto "Butterfly factorization"

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Zheng, Léon. "Frugalité en données et efficacité computationnelle dans l'apprentissage profond." Electronic Thesis or Diss., Lyon, École normale supérieure, 2024. http://www.theses.fr/2024ENSL0009.

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Cette thèse s’intéresse à deux enjeux de frugalité et d’efficacité dans l’apprentissage profond moderne : frugalité en données et efficacité en ressources de calcul. Premièrement, nous étudions l’apprentissage auto-supervisé, une approche prometteuse en vision par ordinateur qui ne nécessite pas d’annotations des données pour l'apprentissage de représentations. En particulier, nous proposons d’unifier plusieurs fonctions objectives auto-supervisées dans un cadre de noyaux invariants par rotation, ce qui ouvre des perspectives en termes de réduction de coût de calcul de ces fonctions objectives
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Trabalhos de conferências sobre o assunto "Butterfly factorization"

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Shekofteh, S. Kazem, Christian Alles, and Holger Fröning. "Reducing Memory Requirements for the IPU using Butterfly Factorizations." In SC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis. ACM, 2023. http://dx.doi.org/10.1145/3624062.3624196.

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