Results 21 to 30 of about 391,798 (266)

CMOS Fixed Pattern Noise Elimination Based on Sparse Unidirectional Hybrid Total Variation

Sensors, 2020
With the improvement of semiconductor technology, the performance of CMOS Image Sensor has been greatly improved, reaching the same level as that of CCD in dark current, linearity and readout noise. However, due to the production process, CMOS has higher
Tao Zhang, Xinyang Li, Jianfeng Li, Zhi Xu   +3 more
doaj   +1 more source

Factors of Sparse Polynomials are Sparse.

Electron. Colloquium Comput. Complex., 2014
This paper was removed due to an error in the proof (Claim 4.12 as stated is not true)
Zeev Dvir, Rafael Oliveira 0002
  +6 more sources

Sparse-to-Sparse Training of Diffusion Models [PDF]

CoRR
Diffusion models (DMs) are a powerful type of generative models that have achieved state-of-the-art results in various image synthesis tasks and have shown potential in other domains, such as natural language processing and temporal data modeling.
OLIVEIRA, Inês, MOCANU, Decebal Constantin, LEIVA, Luis A.   +2 more
openaire   +3 more sources

Scalable Low Power Accelerator for Sparse Recurrent Neural Network

天地一体化信息网络, 2023
The use of edge computing devices in bank outlets for passenger flow analysis, security protection, risk prevention and control is increasingly widespread, among which the performance and power consumption of AI reasoning chips have become a very ...
Panshi JIN, Junjie LI, Jingyi WANG, Pengchong LI, Lei XING, Xiaodong LI   +5 more
doaj  

Seismic Data Denoising Based on Sparse and Low-Rank Regularization

Energies, 2020
Seismic denoising is a core task of seismic data processing. The quality of a denoising result directly affects data analysis, inversion, imaging and other applications.
Shu Li, Xi Yang, Haonan Liu, Yuwei Cai, Zhenming Peng   +4 more
doaj   +1 more source

Stabilized Sparse Online Learning for Sparse Data

J. Mach. Learn. Res., 2016
45 pages, 4 ...
Yuting Ma, Tian Zheng
openaire   +4 more sources

GNSS Signal Acquisition Algorithm Based on Two-Stage Compression of Code-Frequency Domain

Applied Sciences, 2022
The recently-emerging compressed sensing (CS) theory makes GNSS signal processing at a sub-Nyquist rate possible if it has a sparse representation in certain domain.
Fangming Zhou, Lulu Zhao, Limin Li, Yifei Hu, Xinglong Jiang, Jinpei Yu, Guang Liang   +6 more
doaj   +1 more source

Sparse coding

Scholarpedia, 2008
The(frequently updated) original version is avalable at http://www.scholarpedia.org/article ...
Peter Földiák, Dominik M. Endres
openaire   +2 more sources

A Sparse Denoising-Based Super-Resolution Method for Scanning Radar Imaging

Remote Sensing, 2021
Scanning radar enables wide-range imaging through antenna scanning and is widely used for radar warning. The Rayleigh criterion indicates that narrow beams of radar are required to improve the azimuth resolution.
Qiping Zhang, Yin Zhang, Yongchao Zhang, Yulin Huang, Jianyu Yang   +4 more
doaj   +1 more source

Doubly Sparse: Sparse Mixture of Sparse Experts for Efficient Softmax Inference

CoRR, 2019
Computations for the softmax function are significantly expensive when the number of output classes is large. In this paper, we present a novel softmax inference speedup method, Doubly Sparse Softmax (DS-Softmax), that leverages sparse mixture of sparse experts to efficiently retrieve top-k classes. Different from most existing methods that require and
Shun Liao, Ting Chen, Tian Lin, Denny Zhou, Chong Wang   +4 more
openaire   +2 more sources