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pro vyhledávání: '"Morozov, Stanislav"'
Nowadays, low-rank approximations of matrices are an important component of many methods in science and engineering. Traditionally, low-rank approximations are considered in unitary invariant norms, however, recently element-wise approximations have
Externí odkaz:
http://arxiv.org/abs/2410.05247
The problem of low rank approximation is ubiquitous in science. Traditionally this problem is solved in unitary invariant norms such as Frobenius or spectral norm due to existence of efficient methods for building approximations. However, recent resu
Externí odkaz:
http://arxiv.org/abs/2212.01438
The low-rank matrix approximation problem is ubiquitous in computational mathematics. Traditionally, this problem is solved in spectral or Frobenius norms, where the accuracy of the approximation is related to the rate of decrease of the singular val
Externí odkaz:
http://arxiv.org/abs/2201.12301
The recent rise of unsupervised and self-supervised learning has dramatically reduced the dependency on labeled data, providing effective image representations for transfer to downstream vision tasks. Furthermore, recent works employed these represen
Externí odkaz:
http://arxiv.org/abs/2006.04988
Publikováno v:
In Linear Algebra and Its Applications 15 December 2023 679:4-29
Learning useful representations is a key ingredient to the success of modern machine learning. Currently, representation learning mostly relies on embedding data into Euclidean space. However, recent work has shown that data in some domains is better
Externí odkaz:
http://arxiv.org/abs/1910.03524
Nowadays, deep neural networks (DNNs) have become the main instrument for machine learning tasks within a wide range of domains, including vision, NLP, and speech. Meanwhile, in an important case of heterogenous tabular data, the advantage of DNNs ov
Externí odkaz:
http://arxiv.org/abs/1909.06312
Autor:
Morozov, Stanislav, Babenko, Artem
In plenty of machine learning applications, the most relevant items for a particular query should be efficiently extracted, while the relevance function is based on a highly-nonlinear model, e.g., DNNs or GBDTs. Due to the high computational complexi
Externí odkaz:
http://arxiv.org/abs/1908.06887
Autor:
Morozov, Stanislav, Babenko, Artem
We tackle the problem of unsupervised visual descriptors compression, which is a key ingredient of large-scale image retrieval systems. While the deep learning machinery has benefited literally all computer vision pipelines, the existing state-of-the
Externí odkaz:
http://arxiv.org/abs/1908.03883
Autor:
Morozov, Stanislav V.1 morozov_s@bsu.edu.ru, Krupskaya, Svetlana Y.1 krupskaya@bsu.edu.ru, Orekhova, Marina S.1 orechova@bsu.edu.ru, Timoshkova, Olga A.1 timoshkova@bsu.edu.ru, Oleinik, Alexander N.2 oleynik.oleks@gmail.com
Publikováno v:
Journal of Research of the University of Quindio / Revista de Investigaciones Universidad del Quindio. 2022 Supplement, Vol. 34, p6-12. 7p.