Results 11 to 20 of about 13,997,719 (325)
Beyond Low Rank + Sparse: Multi-scale Low Rank Matrix Decomposition [PDF]
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016 We present a natural generalization of the recent low rank + sparse matrix decomposition and consider the decomposition of matrices into components of multiple scales.
Lustig, Michael, Ong, Frank
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Multi-resolution Low-rank Tensor Formats [PDF]
SIAM Journal on Matrix Analysis and Applications, 2020 We describe a simple, black-box compression format for tensors with a multiscale structure. By representing the tensor as a sum of compressed tensors defined on increasingly coarse grids, we capture low-rank structures on each grid-scale, and we show how
Karaman, Sertac, Mickelin, Oscar
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IET Signal Processing, 2022 Isomap is a well‐known nonlinear dimensionality reduction method that highly suffers from computational complexity. Its computational complexity mainly arises from two stages; a) embedding a full graph on the data in the ambient space, and b) a complete ...
Eysan Mehrbani, Mohammad Hossein Kahaei
doaj +3 more sources
Low rank Multivariate regression [PDF]
Electronic Journal of Statistics, 2010 We consider in this paper the multivariate regression problem, when the target regression matrix $A$ is close to a low rank matrix. Our primary interest in on the practical case where the variance of the noise is unknown.
Giraud, Christophe
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From low-rank retractions to dynamical low-rank approximation and back. [PDF]
BIT Numer MathAbstractIn algorithms for solving optimization problems constrained to a smooth manifold, retractions are a well-established tool to ensure that the iterates stay on the manifold. More recently, it has been demonstrated that retractions are a useful concept for other computational tasks on manifold as well, including interpolation tasks.
Séguin A, Ceruti G, Kressner D.
europepmc +5 more sources
Neural oscillation in low-rank SNNs: bridging network dynamics and cognitive function [PDF]
Frontiers in Computational NeuroscienceNeural oscillation, particularly gamma oscillation, are fundamental to cognitive processes such as attention, perception, and decision-making. Experimental studies have shown that the phase of gamma oscillation modulates neuronal response selectivity ...
Bin Li +5 more
doaj +2 more sources
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. Carrell +4 more
semanticscholar +3 more sources
arXiv.orgWe introduce a new logic for describing properties of graphs, which we call low rank MSO. This is the fragment of monadic second-order logic in which set quantification is restricted to vertex sets of bounded cutrank.
Mikolaj Boja'nczyk +4 more
semanticscholar +3 more sources
Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models [PDF]
Neural Information Processing Systems, 2023 Public large-scale text-to-image diffusion models, such as Stable Diffusion, have gained significant attention from the community. These models can be easily customized for new concepts using low-rank adaptations (LoRAs).
Yuchao Gu +12 more
semanticscholar +1 more source
Low Rank Regularization: A review [PDF]
Neural Networks, 2021 Low rank regularization, in essence, involves introducing a low rank or approximately low rank assumption for matrix we aim to learn, which has achieved great success in many fields including machine learning, data mining and computer version. Over the last decade, much progress has been made in theories and practical applications.
Zhanxuan Hu +3 more
openaire +3 more sources