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ECG autoencoder based on low-rank attention. [PDF]

Sci Rep
Zhang S, Fang Y, Ren Y.
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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

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
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Low-Rank Preserving Projections

IEEE Transactions on Cybernetics, 2016
As one of the most popular dimensionality reduction techniques, locality preserving projections (LPP) has been widely used in computer vision and pattern recognition. However, in practical applications, data is always corrupted by noises. For the corrupted data, samples from the same class may not be distributed in the nearest area, thus LPP may lose ...
Lu, Yuwu, Lai, Zhihui, Xu, Yong, Li, Xuelong, Zhang, David, Yuan, Chun   +5 more
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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
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Convex Low Rank Approximation

International Journal of Computer Vision, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Larsson, Viktor, Olsson, Carl
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Low-Rank Tensor Based Proximity Learning for Multi-View Clustering

IEEE Transactions on Knowledge and Data Engineering, 2023
Graph-oriented multi-view clustering methods have achieved impressive performances by employing relationships and complex structures hidden in multi-view data. However, most of them still suffer from the following two common problems.
Mansheng Chen, Changdong Wang, J. Lai
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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
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Reduced Basis Methods: From Low-Rank Matrices to Low-Rank Tensors

SIAM Journal on Scientific Computing, 2016
Summary: We propose a novel combination of the reduced basis method with low-rank tensor techniques for the efficient solution of parameter-dependent linear systems in the case of several parameters. This combination, called rbTensor, consists of three ingredients. First, the underlying parameter-dependent operator is approximated by an explicit affine
Ballani, Jonas, Kressner, Daniel
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Low-Rank High-Order Tensor Completion With Applications in Visual Data

IEEE Transactions on Image Processing, 2022
Recently, tensor Singular Value Decomposition (t-SVD)-based low-rank tensor completion (LRTC) has achieved unprecedented success in addressing various pattern analysis issues. However, existing studies mostly focus on third-order tensors while order- $d$
Wenjin Qin, Hailin Wang, Feng Zhang, Jianjun Wang, Xin Luo, Tingwen Huang   +5 more
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