Results 1 to 10 of about 459,225 (113)
Adaptive quantile low-rank matrix factorization [PDF]
Pattern Recognition, 2020 Low-rank matrix factorization (LRMF) has received much popularity owing to its successful applications in both computer vision and data mining. By assuming noise to come from a Gaussian, Laplace or mixture of Gaussian distributions, significant efforts have been made on optimizing the (weighted) $L_1$ or $L_2$-norm loss between an observed matrix and ...
Shuang Xu, Chunxia Zhang, Jiangshe Zhang
openaire +2 more sources
Adaptive Low-Rank Approximation of Collocation Matrices [PDF]
Computing, 2003 In this paper there is dealt with the solution of integral equations using collocation methods with almost linear complexity. There are used fast multipole, panel clustering and \(H\)-matrix methods which gain their efficiency from approximating the kernel function. The proposed \(H\)-matrix algorithm is purely algebraic.
Bebendorf, M., Rjasanow, S.
openaire +2 more sources
Adapting Regularized Low-Rank Models for Parallel Architectures [PDF]
SIAM Journal on Scientific Computing, 2019 We introduce a reformulation of regularized low-rank recovery models to take advantage of GPU, multiple CPU, and hybridized architectures. Low-rank recovery often involves nuclear-norm minimization through iterative thresholding of singular values. These models are slow to fit and difficult to parallelize because of their dependence on computing a ...
Derek Driggs +2 more
openaire +3 more sources
Poisson Matrix Completion [PDF]
, 2015 We extend the theory of matrix completion to the case where we make Poisson observations for a subset of entries of a low-rank matrix. We consider the (now) usual matrix recovery formulation through maximum likelihood with proper constraints on the ...
Cao, Yang, Xie, Yao
core +1 more source
A Practical Cooperative Multicell MIMO-OFDMA Network Based on Rank Coordination [PDF]
, 2013 An important challenge of wireless networks is to boost the cell edge performance and enable multi-stream transmissions to cell edge users. Interference mitigation techniques relying on multiple antennas and coordination among cells are nowadays heavily ...
Clerckx, Bruno +3 more
core +3 more sources
Continual Learning with Low Rank Adaptation
, 2023 Recent work using pretrained transformers has shown impressive performance when fine-tuned with data from the downstream problem of interest. However, they struggle to retain that performance when the data characteristics changes. In this paper, we focus on continual learning, where a pre-trained transformer is updated to perform well on new data ...
Wistuba, Martin +3 more
openaire +2 more sources
Union Mediation and Adaptation to Reciprocal Loyalty Arrangements [PDF]
, 2009 This study assesses the industrial relations application of the “loyalty-exit-voice” proposition. The loyalty concept is linked to reciprocal employer-employee arrangements and examined as a job attribute in a vignette questionnaire distributed to low ...
Panos, Georgios A, Theodossiou, Ioannis
core +3 more sources
Residual Parameter Transfer for Deep Domain Adaptation [PDF]
, 2017 The goal of Deep Domain Adaptation is to make it possible to use Deep Nets trained in one domain where there is enough annotated training data in another where there is little or none.
Fua, Pascal +2 more
core +2 more sources
Accurate Tensor Completion via Adaptive Low-Rank Representation [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2020 Low-rank representation-based approaches that assume low-rank tensors and exploit their low-rank structure with appropriate prior models have underpinned much of the recent progress in tensor completion. However, real tensor data only approximately comply with the low-rank requirement in most cases, viz., the tensor consists of low-rank (e.g ...
Lei Zhang +5 more
openaire +3 more sources