Results 31 to 40 of about 257,716 (284)
Insights from classifying visual concepts with multiple kernel learning. [PDF]
PLoS ONE, 2012 Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is usually unknown in practical applications. Multiple kernel learning (MKL)
Alexander Binder +7 more
doaj +1 more source
Lower bounds for sparse matrix vector multiplication on hypercubic networks [PDF]
Discrete Mathematics & Theoretical Computer Science, 1998 In this paper we consider the problem of computing on a local memory machine the product y = Ax,where A is a random n×n sparse matrix with Θ(n) nonzero elements.
Giovanni Manzini
doaj +2 more sources
Enabling Massive Deep Neural Networks with the GraphBLAS
, 2017 Deep Neural Networks (DNNs) have emerged as a core tool for machine learning. The computations performed during DNN training and inference are dominated by operations on the weight matrices describing the DNN.
Kepner, Jeremy +5 more
core +1 more source
Compute Less to Get More: Using ORC to Improve Sparse Filtering [PDF]
, 2015 Sparse Filtering is a popular feature learning algorithm for image classification pipelines. In this paper, we connect the performance of Sparse Filtering with spectral properties of the corresponding feature matrices.
Guadarrama, Sergio, Lederer, Johannes
core +1 more source
Sparse Recovery With Block Multiple Measurement Vectors Algorithm
IEEE Access, 2019 This paper investigates the performance of the block multiple measurement vectors (BMMV) algorithm in reconstructing block joint sparse matrices. We prove that if 41) obeys block restricted isometry property with 8 K+1 <; Nf +1 , then BMMV perfectly ...
Yanli Shi, Libo Wang, Rong Luo
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IEEE Access, 2020 Existing Dictionary Learning and Sparse Coding (DLSC) algorithms for Symmetric Positive Definite (SPD) matrices usually adopt Reproducing Kernel Hilbert Space as workspace to perform necessary linear operations.
Yang Zhang, Yuesheng Zhu
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Direct multiplicative methods for sparse matrices. Linear programming [PDF]
Компьютерные исследования и моделирование, 2017 Multiplicative methods for sparse matrices are best suited to reduce the complexity of operations solving systems of linear equations performed on each iteration of the simplex method.
Anastasiya Borisovna Sviridenko
doaj +1 more source
Quantifying the Effect of Matrix Structure on Multithreaded Performance of the SpMV Kernel
, 2014 Sparse matrix-vector multiplication (SpMV) is the core operation in many common network and graph analytics, but poor performance of the SpMV kernel handicaps these applications.
Keltcher, Paul +3 more
core +1 more source
Direct multiplicative methods for sparse matrices. Quadratic programming [PDF]
Компьютерные исследования и моделирование, 2018 A numerically stable direct multiplicative method for solving systems of linear equations that takes into account the sparseness of matrices presented in a packed form is considered.
Anastasiya Borisovna Sviridenko
doaj +1 more source
Estimating sparse precision matrices [PDF]
Monthly Notices of the Royal Astronomical Society, 2016 We apply a method recently introduced to the statistical literature to directly estimate the precision matrix from an ensemble of samples drawn from a corresponding Gaussian distribution. Motivated by the observation that cosmological precision matrices are often approximately sparse, the method allows one to exploit this sparsity of the precision ...
Padmanabhan, Nikhil +3 more
openaire +4 more sources