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Table representation of search results timeline featuring number of search results per year.

Year Number of Results
1959 1
1964 1
1977 1
1979 1
1980 1
1981 1
1982 4
1984 1
1985 1
1986 1
1987 2
1988 1
1989 2
1990 3
1991 5
1992 2
1993 4
1994 6
1995 6
1996 6
1997 9
1998 6
1999 6
2000 8
2001 14
2002 10
2003 8
2004 24
2005 13
2006 25
2007 28
2008 31
2009 29
2010 29
2011 44
2012 47
2013 43
2014 42
2015 41
2016 46
2017 41
2018 57
2019 47
2020 53
2021 65
2022 66
2023 73
2024 28

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883 results

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Page 1
Effective matrix designs for COVID-19 group testing.
Brust D, Brust JJ. Brust D, et al. BMC Bioinformatics. 2023 Jan 24;24(1):26. doi: 10.1186/s12859-023-05145-y. BMC Bioinformatics. 2023. PMID: 36694117 Free PMC article. Review.
This expands the admissible parameter space for the construction of pooling matrices compared to current methods. RESULTS: By arranging samples in a grid and using polynomials to construct pools, we develop direct formulas for an Algorithm (Polynomial Pools (PP)) to …
This expands the admissible parameter space for the construction of pooling matrices compared to current methods. RESULTS: By arranging samp …
Quantum-Inspired Support Vector Machine.
Ding C, Bao TY, Huang HL. Ding C, et al. IEEE Trans Neural Netw Learn Syst. 2022 Dec;33(12):7210-7222. doi: 10.1109/TNNLS.2021.3084467. Epub 2022 Nov 30. IEEE Trans Neural Netw Learn Syst. 2022. PMID: 34111003
Support vector machine (SVM) is a particularly powerful and flexible supervised learning model that analyzes data for both classification and regression, whose usual algorithm complexity scales polynomially with the dimension of data space and the number of data points. .. …
Support vector machine (SVM) is a particularly powerful and flexible supervised learning model that analyzes data for both classification an …
Notes on density matrix perturbation theory.
Truflandier LA, Dianzinga RM, Bowler DR. Truflandier LA, et al. J Chem Phys. 2020 Oct 28;153(16):164105. doi: 10.1063/5.0022244. J Chem Phys. 2020. PMID: 33138442
Density matrix perturbation theory (DMPT) is known as a promising alternative to the Rayleigh-Schrodinger perturbation theory, in which the sum-over-states (SOS) is replaced by algorithms with perturbed density matrices as the input variables. ...The HPCP-DMPT demonstrates …
Density matrix perturbation theory (DMPT) is known as a promising alternative to the Rayleigh-Schrodinger perturbation theory, in whi …
Matrix Product Belief Propagation for reweighted stochastic dynamics over graphs.
Crotti S, Braunstein A. Crotti S, et al. Proc Natl Acad Sci U S A. 2023 Nov 21;120(47):e2307935120. doi: 10.1073/pnas.2307935120. Epub 2023 Nov 14. Proc Natl Acad Sci U S A. 2023. PMID: 37963253
While many existing methods can accurately describe typical realizations of such processes, computing properties of extremely rare events is a hard task, particularly so in the case of recurrent models, in which variables may return to a previously visited state. Here, we build o …
While many existing methods can accurately describe typical realizations of such processes, computing properties of extremely rare events is …
Fast Differentiable Matrix Square Root and Inverse Square Root.
Song Y, Sebe N, Wang W. Song Y, et al. IEEE Trans Pattern Anal Mach Intell. 2023 Jun;45(6):7367-7380. doi: 10.1109/TPAMI.2022.3216339. Epub 2023 May 5. IEEE Trans Pattern Anal Mach Intell. 2023. PMID: 36269908
Computing the matrix square root and its inverse in a differentiable manner is important in a variety of computer vision tasks. ...In this paper, we propose two more efficient variants to compute the differentiable matrix square root and the inverse square root. For …
Computing the matrix square root and its inverse in a differentiable manner is important in a variety of computer vision tasks. ...In …
Equivalent Dynamic Models.
Molenaar PCM. Molenaar PCM. Multivariate Behav Res. 2017 Mar-Apr;52(2):242-258. doi: 10.1080/00273171.2016.1277681. Epub 2017 Feb 16. Multivariate Behav Res. 2017. PMID: 28207288
Christoffel-Darboux sources.
Martínez-Herrero R, Gori F. Martínez-Herrero R, et al. Opt Lett. 2021 Mar 1;46(5):973-976. doi: 10.1364/OL.417534. Opt Lett. 2021. PMID: 33649634
After discussing general properties of CD kernels, a specific example is worked out using Hermite polynomials. A connection with the density matrix will be highlighted....
After discussing general properties of CD kernels, a specific example is worked out using Hermite polynomials. A connection with the …
Efficient Approximations for Matrix-Based Renyi's Entropy on Sequential Data.
Dong Y, Gong T, Chen H, Li C. Dong Y, et al. IEEE Trans Neural Netw Learn Syst. 2023 Sep 19;PP. doi: 10.1109/TNNLS.2023.3314089. Online ahead of print. IEEE Trans Neural Netw Learn Syst. 2023. PMID: 37725746
The matrix-based Renyi's entropy (MBRE) has recently been introduced as a substitute for the original Renyi's entropy that could be directly obtained from data samples, avoiding the expensive intermediate step of density estimation. ...Specifically, assuming that the chang …
The matrix-based Renyi's entropy (MBRE) has recently been introduced as a substitute for the original Renyi's entropy that could be d …
Hermitian-Randic matrix and Hermitian-Randic energy of mixed graphs.
Lu Y, Wang L, Zhou Q. Lu Y, et al. J Inequal Appl. 2017;2017(1):54. doi: 10.1186/s13660-017-1329-8. Epub 2017 Mar 3. J Inequal Appl. 2017. PMID: 28316452 Free PMC article.
Let M be a mixed graph and [Formula: see text] be its Hermitian-adjacency matrix. If we add a Randic weight to every edge and arc in M, then we can get a new weighted Hermitian-adjacency matrix. ...In this paper, firstly, we compute the characteristic polynomial …
Let M be a mixed graph and [Formula: see text] be its Hermitian-adjacency matrix. If we add a Randic weight to every edge and arc in …
Straggler- and Adversary-Tolerant Secure Distributed Matrix Multiplication Using Polynomial Codes.
Byrne E, Gnilke OW, Kliewer J. Byrne E, et al. Entropy (Basel). 2023 Jan 31;25(2):266. doi: 10.3390/e25020266. Entropy (Basel). 2023. PMID: 36832632 Free PMC article.
Large matrix multiplications commonly take place in large-scale machine-learning applications. ...Specifically, we assume that workers can collude and eavesdrop on the content of these matrices. For this problem, we introduce a new class of polynomial codes with few …
Large matrix multiplications commonly take place in large-scale machine-learning applications. ...Specifically, we assume that worker …
883 results