Results 241 to 250 of about 14,259,947 (309)

A Novel Low-Rank Embedded Latent Multi-View Subspace Clustering Approach. [PDF]

Sensors (Basel)
Wang S, Chen L, Liang Z, Liu Q.
europepmc   +1 more source

Low-Rank Thinning

CoRR
The goal in thinning is to summarize a dataset using a small set of representative points. Remarkably, sub-Gaussian thinning algorithms like Kernel Halving and Compress can match the quality of uniform subsampling while substantially reducing the number ...
A. Carrell, Albert Gong, Abhishek Shetty, Raaz Dwivedi, L. Mackey   +4 more
semanticscholar   +4 more sources

Low rank MSO

CoRR
We introduce a new logic for describing properties of graphs, which we call low rank MSO. This is the fragment of monadic second-order logic in which set quantification is restricted to vertex sets of bounded cutrank.
Mikolaj Boja'nczyk, Michał Pilipczuk, Wojciech Przybyszewski, Marek Sokołowski, Giannos Stamoulis   +4 more
semanticscholar   +3 more sources

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Low-rank and sparse matrices fitting algorithm for low-rank representation

Computers & Mathematics with Applications, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jianxi Zhao, Lina Zhao 0002
openaire   +2 more sources

Hyperspectral Image Denoising via Tensor Low-Rank Prior and Unsupervised Deep Spatial–Spectral Prior

IEEE Transactions on Geoscience and Remote Sensing, 2022
Hyperspectral image (HSI) denoising is a fundamental task in remote sensing image processing, which is helpful for HSI subsequent applications, such as unmixing and classification.
Wei-Hao Wu, Ting-Zhu Huang, Xile Zhao, Jian-Li Wang, Yu-Bang Zheng   +4 more
semanticscholar   +1 more source

Convex Low Rank Approximation

International Journal of Computer Vision, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Viktor Larsson, Carl Olsson
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Sparse Low-rank Adaptation of Pre-trained Language Models

Conference on Empirical Methods in Natural Language Processing, 2023
Fine-tuning pre-trained large language models in a parameter-efficient manner is widely studied for its effectiveness and efficiency. The popular method of low-rank adaptation (LoRA) offers a notable approach, hypothesizing that the adaptation process is
Ning Ding, Xingtai Lv, Qiaosen Wang, Yulin Chen, Bowen Zhou, Zhiyuan Liu, Maosong Sun   +6 more
semanticscholar   +1 more source

On the Compression of Low Rank Matrices

SIAM Journal on Scientific Computing, 2005
The authors describe a procedure for the decomposition and compression of low-rank matrices. Such matrices arise for instance in computational physics in potential theory, in fluid dynamics, in numerical simulations of electromagnetic phenomena. The decomposition of a matrix \(A\) of rank \(k\) is constructed in the form \(A=U\circ B\circ V^*\), where \
Hongwei Cheng, Zydrunas Gimbutas, Per-Gunnar Martinsson, Vladimir Rokhlin   +3 more
openaire   +1 more source

The Expressive Power of Low-Rank Adaptation

International Conference on Learning Representations, 2023
Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning method that leverages low-rank adaptation of weight matrices, has emerged as a prevalent technique for fine-tuning pre-trained models such as large language models and diffusion models ...
Yuchen Zeng, Kangwook Lee
semanticscholar   +1 more source

LRTCFPan: Low-Rank Tensor Completion Based Framework for Pansharpening

IEEE Transactions on Image Processing, 2023
Pansharpening refers to the fusion of a low spatial-resolution multispectral image with a high spatial-resolution panchromatic image. In this paper, we propose a novel low-rank tensor completion (LRTC)-based framework with some regularizers for ...
Zhong-Cheng Wu, Ting-Zhu Huang, Liang-Jian Deng, Jie Huang, J. Chanussot, G. Vivone   +5 more
semanticscholar   +1 more source