Results 31 to 40 of about 12,418,444 (305)
A Non-Local Low-Rank Algorithm for Sub-Bottom Profile Sonar Image Denoising
Remote Sensing, 2020 Due to the influence of equipment instability and surveying environment, scattering echoes and other factors, it is sometimes difficult to obtain high-quality sub-bottom profile (SBP) images by traditional denoising methods.
Shaobo Li +4 more
doaj +1 more source
Tensor Completion via Smooth Rank Function Low-Rank Approximate Regularization
Remote Sensing, 2023 In recent years, the tensor completion algorithm has played a vital part in the reconstruction of missing elements within high-dimensional remote sensing image data.
Shicheng Yu +5 more
doaj +1 more source
Low-Rank Few-Shot Adaptation of Vision-Language Models [PDF]
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)Recent progress in the few-shot adaptation of VisionLanguage Models (VLMs) has further pushed their generalization capabilities, at the expense of just a few labeled samples within the target downstream task.
Maxime Zanella, Ismail Ben Ayed
semanticscholar +1 more source
Low-rank Linear Fluid-structure Interaction Discretizations [PDF]
, 2020 Fluid-structure interaction models involve parameters that describe the solid and the fluid behavior. In simulations, there often is a need to vary these parameters to examine the behavior of a fluid-structure interaction model for different solids and ...
Benner, Peter +2 more
core +2 more sources
Seismic Data Denoising Based on Sparse and Low-Rank Regularization
Energies, 2020 Seismic denoising is a core task of seismic data processing. The quality of a denoising result directly affects data analysis, inversion, imaging and other applications.
Shu Li +4 more
doaj +1 more source
Fractional laplacians viscoelastic wave equation low-rank temporal extrapolation
Frontiers in Earth Science, 2023 The fractional Laplacians constant-Q (FLCQ) viscoelastic wave equation can describe seismic wave propagation accurately in attenuating media. A staggered-grid pseudo-spectral (SGPS) method is usually applied to solve this wave equation but it is of only ...
Hanming Chen +8 more
doaj +1 more source
Compressing Transformers: Features Are Low-Rank, but Weights Are Not!
AAAI Conference on Artificial Intelligence, 2023 Transformer and its variants achieve excellent results in various computer vision and natural language processing tasks, but high computational costs and reliance on large training datasets restrict their deployment in resource-constrained settings.
Hao Yu, Jianxin Wu
semanticscholar +1 more source
Separable and Low-Rank Continuous Games [PDF]
, 2007 In this paper, we study nonzero-sum separable games, which are continuous games whose payoffs take a sum-of-products form. Included in this subclass are all finite games and polynomial games. We investigate the structure of equilibria in separable games.
Asuman Ozdaglar +17 more
core +5 more sources
Smooth Non-negative Low-Rank Graph Representation for Clustering [PDF]
Jisuanji kexue yu tansuoThe existing low-rank graph representation algorithms fail to capture the global representation structure of data accurately, and cannot make full use of the valid information of data to guide the construction of the representation graph, then the ...
QIAN Luoxiong, CHEN Mei, ZHANG Chi, ZHANG Jinhong, MA Xueyan
doaj +1 more source
, 2021 We consider the problem of forecasting multiple values of the future of a vector time series, using some past values. This problem, and related ones such as one-step-ahead prediction, have a very long history, and there are a number of well-known methods for it, including vector auto-regressive models, state-space methods, multi-task regression, and ...
Barratt, Shane +2 more
openaire +2 more sources