Results 31 to 40 of about 14,259,947 (309)
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
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CoRR, 2021 The latent space of a Generative Adversarial Network (GAN) has been shown to encode rich semantics within some subspaces. To identify these subspaces, researchers typically analyze the statistical information from a collection of synthesized data, and the identified subspaces tend to control image attributes globally (i.e., manipulating an attribute ...
Jiapeng Zhu 0001 +6 more
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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.
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Journal of Mathematical Physics, 2023 We present a method to generate all fusion rings of a specific rank and multiplicity. This method generated exhaustive lists of fusion rings up to order 9 for several multiplicities. We introduce a class of non-commutative fusion rings based on a group with transitive action on a set. This construction generalises the Tambara–Yamagami (TY) and Haagerup-
G. Vercleyen, J. K. Slingerland
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Efficient Low-rank Multimodal Fusion With Modality-Specific Factors [PDF]
Annual Meeting of the Association for Computational Linguistics, 2018 Multimodal research is an emerging field of artificial intelligence, and one of the main research problems in this field is multimodal fusion. The fusion of multimodal data is the process of integrating multiple unimodal representations into one compact ...
Zhun Liu +5 more
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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
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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
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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
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Multi-Task Dense Prediction via Mixture of Low-Rank Experts [PDF]
Computer Vision and Pattern RecognitionPrevious multitask dense prediction methods based on the Mixture of Experts (MoE) have received great performance but they neglect the importance of explicitly modeling the global relations among all tasks.
Yuqi Yang +5 more
semanticscholar +1 more source
Reduced Basis Methods: From Low-Rank Matrices to Low-Rank Tensors
SIAM Journal on Scientific Computing, 2016 Summary: We propose a novel combination of the reduced basis method with low-rank tensor techniques for the efficient solution of parameter-dependent linear systems in the case of several parameters. This combination, called rbTensor, consists of three ingredients. First, the underlying parameter-dependent operator is approximated by an explicit affine
Ballani, Jonas, Kressner, Daniel
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