Results 11 to 20 of about 3,401,789 (270)
Advances in Combinatorics, 2022 Let $\mathbf{A}$ be an $n\times n$-matrix over $\mathbb{F}_2$ whose every entry equals $1$ with probability $d/n$ independently for a fixed $d>0$. Draw a vector $\mathbf{y}$ randomly from the column space of $\mathbf{A}$. It is a simple observation that the entries of a random solution $\mathbf{x}$ to $\mathbf{A} x=\mathbf{y}$ are asymptotically ...
Coja-Oghlan, Amin +4 more
openaire +2 more sources
Kernelized Sparse Bayesian Matrix Factorization
IEEE Transactions on Neural Networks and Learning Systems, 2021 Extracting low-rank and/or sparse structures using matrix factorization techniques has been extensively studied in the machine learning community. Kernelized matrix factorization (KMF) is a powerful tool to incorporate side information into the low-rank approximation model, which has been applied to solve the problems of data mining, recommender ...
Caoyuan Li +5 more
openaire +4 more sources
Fast Sparse Matrix Multiplication [PDF]
ACM Transactions on Algorithms, 2004 Let A and B two n × n matrices over a ring R (e.g., the reals or the integers) each containing at most m nonzero elements. We present a new algorithm that multiplies A and
Raphael Yuster, Uri Zwick
openaire +1 more source
A Systematic Survey of General Sparse Matrix-matrix Multiplication [PDF]
ACM Computing Surveys, 2020 General Sparse Matrix-Matrix Multiplication (SpGEMM) has attracted much attention from researchers in graph analyzing, scientific computing, and deep learning.
Jianhua Gao +3 more
semanticscholar +1 more source
Group-sparse matrix recovery [PDF]
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014 Comment: ICASSP ...
Zeng, Xiangrong +1 more
openaire +2 more sources
Accelerating Sparse Matrix-Matrix Multiplication with GPU Tensor Cores [PDF]
Computers & electrical engineering, 2020 Sparse general matrix–matrix multiplication (spGEMM) is an essential component in many scientific and data analytics applications. However, the sparsity pattern of the input matrices and the interaction of their patterns make spGEMM challenging.
Orestis Zachariadis +3 more
semanticscholar +1 more source
NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization [PDF]
The Web Conference, 2019 We study the problem of large-scale network embedding, which aims to learn latent representations for network mining applications. Previous research shows that 1) popular network embedding benchmarks, such as DeepWalk, are in essence implicitly ...
J. Qiu +6 more
semanticscholar +1 more source
ACM Transactions on Parallel Computing, 2020 The symmetric sparse matrix-vector multiplication (SymmSpMV) is an important building block for many numerical linear algebra kernel operations or graph traversal applications. Parallelizing SymmSpMV on today’s multicore platforms with up to 100 cores is
C. Alappat +7 more
semanticscholar +1 more source
Sparse deep nonnegative matrix factorization [PDF]
Big Data Mining and Analytics, 2020 13 pages, 8 ...
Zhenxing Guo, Shihua Zhang
openaire +3 more sources
Faster Inversion and Other Black Box Matrix Computations Using Efficient Block Projections [PDF]
, 2007 Block projections have been used, in [Eberly et al. 2006], to obtain an efficient algorithm to find solutions for sparse systems of linear equations.
Eberly, Wayne +4 more
core +6 more sources