Randomized Algorithms for Matrices and Data 作者:Mahoney, Michael W. 出版社:Now Publishers 出版年:2011-11-22 页数:110 定价:USD 80.00 装帧:Paperback 丛书:Foundations and Trends® in Machine Learning ISBN:9781601985064 豆瓣评分
Randomized algorithms for matrices and data. Advances in Machine Learning and Data Mining for Astronomy, CRC Press, Taylor & Francis Group, Eds.: Michael J. Way, Jeffrey D. Scargle, Kamal M. Ali, Ashok N. Srivastava, p. 647-672, 1:647-672, 2012.M. W. Mahoney. Randomized algorithms ...
We present randomized algorithms for estimating the trace and determinant of Hermitian positive semi-definite matrices. The algorithms are based on subspace iteration, and access the matrix only through matrix vector products. We analyse the error due to randomization, for starting guesses whose elements...
Mahoney, M.: Randomized algorithms for matrices and data. Found. Trends Mach. Learn. 3(2), 123–224 (2011) Murata, N.: A Statistical Study of On-Line Learning. Cambridge University Press, Cambridge (1998) Google Scholar Natterer, F.: The Mathematics of Computerized Tomography, volume 32...
We demonstrate for the first time that randomized algorithms can be instilled in transformers through learning, in a purely data- and objective-driven manner. First, we analyze known adversarial objectives for which randomized algorithms offer a distinct advantage over deterministic ones. We then show...
An approach that is recently gaining popularity is that of using randomized algorithms, which can be used to solve a problem approximately, most of the time. We begin with the premise that many problems in robustness analysis and synthesis can be formulated as the minimization of an objective ...
Suppose A B and C are n n matrices with entries from a nite eld F . We wish to verify whether AB = C . Multiplying out takes about n3 steps { this can be cut to O(n2:38) steps using sophisticated algorithms. { Typeset by FoilTEX { 36 Simple randomized algorithm Pick an n-vecto...
Combined with approximation theory19, this leads to state-of-the-art query complexities and an elegant unifying structure for a variety of quantum algorithms of interest. For simplicity of the presentation, in the main text, we focus explicitly on the case of Hermitian matrices. There, QSVT ...
Our algorithms can be extended to numerical attributes. The proposed approach is validated by extensive experiments and comparison with existing randomized response and generalization methods. The contributions of this paper include: • A general framework for data publication is proposed based on ...
See ourprivacy policyfor more information on the use of your personal data. Manage preferencesfor further information and to change your choices. Accept all cookies Abstract It is well known that a family ofcommuting matrices can be simultaneously triangularized by a unitary similarity transformation....