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Low-Rank Tensor Fusion for Enhanced Deep Learning-Based Multimodal Brain Age Estimation. [PDF]
Brain SciLiu X +5 more
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Improved phosphorus MRSI acquisition through compressed sensing acceleration combined with low-rank reconstruction. [PDF]
MAGMASongeon J +8 more
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International Conference on Machine LearningThe 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. M. Carrell +4 more
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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 +4 more
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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 +4 more
semanticscholar +1 more source
The Expressive Power of Low-Rank Adaptation
International Conference on Learning Representations, 2023Low-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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Sparse Low-rank Adaptation of Pre-trained Language Models
Conference on Empirical Methods in Natural Language Processing, 2023Fine-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 +6 more
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Robust Low-Rank Latent Feature Analysis for Spatiotemporal Signal Recovery
IEEE Transactions on Neural Networks and Learning Systems, 2023Wireless sensor network (WSN) is an emerging and promising developing area in the intelligent sensing field. Due to various factors like sudden sensors breakdown or saving energy by deliberately shutting down partial nodes, there are always massive ...
Di Wu +4 more
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Low-Rank Tensor Based Proximity Learning for Multi-View Clustering
IEEE Transactions on Knowledge and Data Engineering, 2023Graph-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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Low-Rank Preserving Projections
IEEE Transactions on Cybernetics, 2016As 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 +5 more
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LRTCFPan: Low-Rank Tensor Completion Based Framework for Pansharpening
IEEE Transactions on Image Processing, 2023Pansharpening 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 +5 more
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