Low-rank matrix approximation [18] aims to represent a matrix with a lower rank than the original, achieving a more compact form while minimizing information loss. This approach allows for more economical storag
low-rank matrix approximationpass-efficient algorithmfixed-precision problemRandomized algorithms for low-rank matrix approximation are investigated, with the emphasis on the fixed-precision problem and computational efficiency for handling large matrices. The algorithms are based on the so-called QB ...
Low-rank approximation with interpretabilityLow-rank approximations based on the selected columns and rows from a given matrix are an alternative approach to singular value decomposition (SVD) and offer more interpretable outputs. They have been successfully used in pattern recognition, recommendation ...
Low-rank matrix approximation is a fundamental tool in data analysis for processing large datasets, reducing noise, and finding important signals. In this work, we present a novel truncated LU factorization called Spectrum-Revealing LU (SRLU) for effective low-rank matrix approximation, and develop...
@inproceedings{pela, author = {Yangyang Guo and Guangzhi Wang and Mohan Kankanhalli}, title = {PELA: Learning Parameter-Efficient Models with Low-Rank Approximation}, booktitle = {CVPR}, year = {2024} }About PELA: Learning Parameter-Efficient Models with Low-Rank Approximation [CVPR 2024] ...
In this work, the low rank approximation concept is extended to the non-equilibrium Green's function (NEGF) method to achieve a very efficient approximated algorithm for coherent and incoherent electron transport. This new method is applied to inelastic transport in various semiconductor nanodevices. ...
We propose a new method for the approximate solution of the Lyapunov equation with rank- 1 1 right-hand side, which is based on extended rational Krylov subspace approximation with adaptively computed shifts. The shift selection is obtained from the connection between the Lyapunov equation, solution...
点击关注@LiteAI,跟进最新Efficient AI & 边缘AI & 模型轻量化技术。 1 WACV 2023 | SVD-NAS Coupling Low-Rank Approximation and Neural Architecture Search 标题:SVD-NAS:低阶近似与神经网络结构搜索的耦合 文章链接:https://arxiv.org/abs/2208.10404 ...
隋阳,目前PhD就读于,研究方向为:Model Compression(Pruning, Low-rank Approximation, Quantization),Efficient Training等,并在NeurIPS,CVPR,AAAI,ISCA发表多篇顶会论文。曾在百度,京东,腾讯美国等实习和工作,是百度深度学习推理框架Paddle-Lite的主要初创人员(目前6.4k stars)。
matrix/low-rank decomposition knowledge distillation (KD) Note, this repo is more about pruning (with lottery ticket hypothesis or LTH as a sub-topic), KD, and quantization. For other topics like NAS, see more comprehensive collections (## Related Repos and Websites) at the end of this fil...