Results 11 to 20 of about 459,286 (299)
A concise analysis of low-rank adaptation [PDF]
Applied and Computational EngineeringRecent years the pre-trained language models have been proved to be a transformative technology within the domain of Natural Language Processing (NLP). From early word embeddings to modern transformer-based architectures, the success of models like BERT, GPT-3, and their variants has led to remarkable advancements in various NLP tasks.
Yanran Chen
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Knit-FLUX: Simulation of Knitted Fabric Images Based on Low-Rank Adaptation of Diffusion Models [PDF]
Applied SciencesGenerative model-assisted design has become a trend, providing a new paradigm for knitted fabric image generation. The FLUX diffusion model was chosen to generate images in this study and was compared to other generative models.
Xiaochen Liu +4 more
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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
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Structure-Aware Low-Rank Adaptation for Parameter-Efficient Fine-Tuning
Mathematics, 2023 With the growing scale of pre-trained language models (PLMs), full parameter fine-tuning becomes prohibitively expensive and practically infeasible.
Yahao Hu +4 more
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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.
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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
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Simultaneously Improve Transferability and Discriminability for Adversarial Domain Adaptation
Entropy, 2021 Although adversarial domain adaptation enhances feature transferability, the feature discriminability will be degraded in the process of adversarial learning.
Ting Xiao +3 more
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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
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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
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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
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