Results 271 to 280 of about 12,418,444 (305)

Convex Low Rank Approximation

International Journal of Computer Vision, 2016
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Larsson, Viktor, Olsson, Carl
openaire   +1 more source

Low-Rank and Sparse Representation for Hyperspectral Image Processing: A review

IEEE Geoscience and Remote Sensing Magazine, 2022
Combining rich spectral and spatial information, a hyperspectral image (HSI) can provide a more comprehensive characterization of the Earth’s surface. To better exploit HSIs, a large number of algorithms have been developed during the past few decades ...
Jiangtao Peng, Weiwei Sun, Hengchao Li, Wei Li, Xiangchao Meng, Chiru Ge, Qian Du   +6 more
semanticscholar   +1 more source

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
openaire   +1 more source

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
semanticscholar   +1 more source

Learning Tensor Low-Rank Representation for Hyperspectral Anomaly Detection

IEEE Transactions on Cybernetics, 2022
Recently, low-rank representation (LRR) methods have been widely applied for hyperspectral anomaly detection, due to their potentials in separating the backgrounds and anomalies.
Minghua Wang, Qiang Wang, D. Hong, S. K. Roy, J. Chanussot   +4 more
semanticscholar   +1 more source

DoRA: Weight-Decomposed Low-Rank Adaptation

International Conference on Machine Learning
Among the widely used parameter-efficient fine-tuning (PEFT) methods, LoRA and its variants have gained considerable popularity because of avoiding additional inference costs.
Shih-Yang Liu, Chien-Yi Wang, Hongxu Yin, Pavlo Molchanov, Yu-Chiang Frank Wang, Kwang-Ting Cheng, Min-Hung Chen   +6 more
semanticscholar   +1 more source

GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

International Conference on Machine Learning
Training Large Language Models (LLMs) presents significant memory challenges, predominantly due to the growing size of weights and optimizer states. Common memory-reduction approaches, such as low-rank adaptation (LoRA), add a trainable low-rank matrix ...
Jiawei Zhao, Zhenyu (Allen) Zhang, Beidi Chen, Zhangyang Wang, Anima Anandkumar, Yuandong Tian   +5 more
semanticscholar   +1 more source

The low-rank hypothesis of complex systems

Nature Physics, 2022
Complex systems are high-dimensional nonlinear dynamical systems with heterogeneous interactions among their constituents. To make interpretable predictions about their large-scale behaviour, it is typically assumed that these dynamics can be reduced to ...
Vincent Thibeault, Antoine Allard, Patrick Desrosiers   +2 more
semanticscholar   +1 more source

LoRA+: Efficient Low Rank Adaptation of Large Models

International Conference on Machine Learning
In this paper, we show that Low Rank Adaptation (LoRA) as originally introduced in Hu et al. (2021) leads to suboptimal finetuning of models with large width (embedding dimension). This is due to the fact that adapter matrices A and B in LoRA are updated
Soufiane Hayou, Nikhil Ghosh, Bin Yu
semanticscholar   +1 more source

Hyperspectral Image Denoising Using Factor Group Sparsity-Regularized Nonconvex Low-Rank Approximation

IEEE Transactions on Geoscience and Remote Sensing, 2022
Hyperspectral image (HSI) mixed noise removal is a fundamental problem and an important preprocessing step in remote sensing fields. The low-rank approximation-based methods have been verified effective to encode the global spectral correlation for HSI ...
Yong Chen, Tingzhu Huang, Wei He, Xile Zhao, Hongyan Zhang, Jinshan Zeng   +5 more
semanticscholar   +1 more source