Literatura académica sobre el tema "Butterfly factorization"
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Artículos de revistas sobre el tema "Butterfly factorization"
Li, Yingzhou, Haizhao Yang, Eileen R. Martin, Kenneth L. Ho y Lexing Ying. "Butterfly Factorization". Multiscale Modeling & Simulation 13, n.º 2 (enero de 2015): 714–32. http://dx.doi.org/10.1137/15m1007173.
Texto completoLi, Yingzhou y Haizhao Yang. "Interpolative Butterfly Factorization". SIAM Journal on Scientific Computing 39, n.º 2 (enero de 2017): A503—A531. http://dx.doi.org/10.1137/16m1074941.
Texto completoLi, Yingzhou, Haizhao Yang y Lexing Ying. "Multidimensional butterfly factorization". Applied and Computational Harmonic Analysis 44, n.º 3 (mayo de 2018): 737–58. http://dx.doi.org/10.1016/j.acha.2017.04.002.
Texto completoPang, Qiyuan, Kenneth L. Ho y Haizhao Yang. "Interpolative Decomposition Butterfly Factorization". SIAM Journal on Scientific Computing 42, n.º 2 (enero de 2020): A1097—A1115. http://dx.doi.org/10.1137/19m1294873.
Texto completoLiu, Yang, Xin Xing, Han Guo, Eric Michielssen, Pieter Ghysels y Xiaoye Sherry Li. "Butterfly Factorization Via Randomized Matrix-Vector Multiplications". SIAM Journal on Scientific Computing 43, n.º 2 (enero de 2021): A883—A907. http://dx.doi.org/10.1137/20m1315853.
Texto completoChen, Ze, Juan Zhang, Kenneth L. Ho y Haizhao Yang. "Multidimensional phase recovery and interpolative decomposition butterfly factorization". Journal of Computational Physics 412 (julio de 2020): 109427. http://dx.doi.org/10.1016/j.jcp.2020.109427.
Texto completoJaber, Marwan A. y Daniel Massicotte. "Radix-2α/4β Building Blocks for Efficient VLSI’s Higher Radices Butterflies Implementation". VLSI Design 2014 (13 de mayo de 2014): 1–13. http://dx.doi.org/10.1155/2014/690594.
Texto completoBremer, James, Ze Chen y Haizhao Yang. "Rapid Application of the Spherical Harmonic Transform via Interpolative Decomposition Butterfly Factorization". SIAM Journal on Scientific Computing 43, n.º 6 (enero de 2021): A3789—A3808. http://dx.doi.org/10.1137/20m1333845.
Texto completoYang, Haizhao. "A unified framework for oscillatory integral transforms: When to use NUFFT or butterfly factorization?" Journal of Computational Physics 388 (julio de 2019): 103–22. http://dx.doi.org/10.1016/j.jcp.2019.02.044.
Texto completoMardan, Suha Suliman y Mounir Taha Hamood. "New fast Walsh–Hadamard–Hartley transform algorithm". International Journal of Electrical and Computer Engineering (IJECE) 13, n.º 2 (1 de abril de 2023): 1533. http://dx.doi.org/10.11591/ijece.v13i2.pp1533-1540.
Texto completoTesis sobre el tema "Butterfly factorization"
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.
Texto completoThis thesis focuses on two challenges of frugality and efficiency in modern deep learning: data frugality and computational resource efficiency. First, we study self-supervised learning, a promising approach in computer vision that does not require data annotations for learning representations. In particular, we propose a unification of several self-supervised objective functions under a framework based on rotation-invariant kernels, which opens up prospects to reduce the computational cost of these objective functions. Second, given that matrix multiplication is the predominant operation in deep neural networks, we focus on the construction of fast algorithms that allow matrix-vector multiplication with nearly linear complexity. More specifically, we examine the problem of sparse matrix factorization under the constraint of butterfly sparsity, a structure common to several fast transforms like the discrete Fourier transform. The thesis establishes new theoretical guarantees for butterfly factorization algorithms, and explores the potential of butterfly sparsity to reduce the computational costs of neural networks during their training or inference phase. In particular, we explore the efficiency of GPU implementations for butterfly sparse matrix multiplication, with the goal of truly accelerating sparse neural networks
Actas de conferencias sobre el tema "Butterfly factorization"
Shekofteh, S. Kazem, Christian Alles y Holger Fröning. "Reducing Memory Requirements for the IPU using Butterfly Factorizations". En SC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3624062.3624196.
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