Results 81 to 90 of about 1,415,945 (160)

A sparse reformulation of the Green's function formalism allows efficient simulations of morphological neuron models

Frontiers in Systems Neuroscience, 2014
Neurons are spatially extended structures with an elaborate dendritic tree that integrates spatio-temporal input patterns. Traditionally, this integration is analysed using compartmental simulations of the cable equation [1].
Willem Wybo, Daniele Boccalini, Benjamin Torben-Nielsen   +2 more
doaj   +1 more source

Sparse regulatory networks

The Annals of Applied Statistics, 2010
Published in at http://dx.doi.org/10.1214/10-AOAS350 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
James, Gareth M., Sabatti, Chiara, Zhou, Nengfeng, Zhu, Ji   +3 more
openaire   +5 more sources

Compressing by Learning in a Low-Rank and Sparse Decomposition Form

IEEE Access, 2019
Low-rankness and sparsity are often used to guide the compression of convolutional neural networks (CNNs) separately. Since they capture global and local structure of a matrix respectively, we combine these two complementary properties together to pursue
Kailing Guo, Xiaona Xie, Xiangmin Xu, Xiaofen Xing   +3 more
doaj   +1 more source

A cooperative stochastic configuration network based on differential evolutionary sparrow search algorithm for prediction

Systems Science & Control Engineering
Stochastic configuration network (SCN) is a powerful prediction model whose performance is significantly influenced by the configuration of the network parameters.
Wenhao Fang, Bo Shen, Anqi Pan, Lei Zou, Baoye Song   +4 more
doaj   +1 more source

Factors of Sparse Polynomials are Sparse

, 2014
This paper was removed due to an error in the proof (Claim 4.12 as stated is not true)
Dvir, Zeev, de Oliveira, Rafael Mendes
openaire   +2 more sources

A CS Recovery Algorithm for Model and Time Delay Identification of MISO-FIR Systems

Algorithms, 2015
This paper considers identifying the multiple input single output finite impulse response (MISO-FIR) systems with unknown time delays and orders. Generally, parameters, orders and time delays of an MISO system are separately identified from different ...
Yanjun Liu, Taiyang Tao
doaj   +1 more source

svt: Singular Value Thresholding in MATLAB

Journal of Statistical Software, 2017
Many statistical learning methods such as matrix completion, matrix regression, and multiple response regression estimate a matrix of parameters. The nuclear norm regularization is frequently employed to achieve shrinkage and low rank solutions.
Cai Li, Hua Zhou
doaj   +1 more source

SAR 3D Reconstruction Based on Multi-Prior Collaboration

Remote Sensing
Array synthetic aperture radar (SAR) three-dimensional (3D) image reconstruction enables the extraction of target distribution information in 3D space, supporting scattering characteristic analysis and structural interpretation.
Yangyang Wang, Zhenxiao Zhou, Zhiming He, Xu Zhan, Jiapan Yu, Xingcheng Han, Xiaoling Zhang, Zhiliang Yang, Jianping An   +8 more
doaj   +1 more source

Integrating Dual Graph Constraints into Sparse Non-Negative Tucker Decomposition for Enhanced Co-Clustering

Mathematics
Collaborative clustering is an ensemble technique that enhances clustering performance by simultaneously and synergistically processing multiple data dimensions or tasks.
Jing Han, Linzhang Lu
doaj   +1 more source

Sparse Recovery Using Sparse Random Matrices [PDF]

, 2010
Over the recent years, a new linear method for compressing high-dimensional data (e.g., images) has been discovered. For any high-dimensional vector x, its sketch is equal to Ax, where A is an m×n matrix (possibly chosen at random). Although typically the sketch length m is much smaller than the number of dimensions n, the sketch contains enough ...
openaire   +1 more source