Zobrazeno 1 - 10
of 127 078
pro vyhledávání: '"P, Tucker"'
Autor:
Aksoy, Doruk, Gorodetsky, Alex A.
We present two new algorithms for approximating and updating the hierarchical Tucker decomposition of tensor streams. The first algorithm, Batch Hierarchical Tucker - leaf to root (BHT-l2r), proposes an alternative and more efficient way of approxima
Externí odkaz:
http://arxiv.org/abs/2412.16544
Autor:
Stolf, Federica, Canale, Antonio
Tucker tensor decomposition offers a more effective representation for multiway data compared to the widely used PARAFAC model. However, its flexibility brings the challenge of selecting the appropriate latent multi-rank. To overcome the issue of pre
Externí odkaz:
http://arxiv.org/abs/2411.10218
Interpreting the decisions of Convolutional Neural Networks (CNNs) is essential for understanding their behavior, yet explainability remains a significant challenge, particularly for self-supervised models. Most existing methods for generating salien
Externí odkaz:
http://arxiv.org/abs/2410.23072
Hyperspectral and multispectral image fusion aims to generate high spectral and spatial resolution hyperspectral images (HR-HSI) by fusing high-resolution multispectral images (HR-MSI) and low-resolution hyperspectral images (LR-HSI). However, existi
Externí odkaz:
http://arxiv.org/abs/2409.09670
To efficiently express tensor data using the Tucker format, a critical task is to minimize the multilinear rank such that the model would not be over-flexible and lead to overfitting. Due to the lack of rank minimization tools in tensor, existing wor
Externí odkaz:
http://arxiv.org/abs/2409.05139
This paper presents a novel approach to solving convex optimization problems by leveraging the fact that, under certain regularity conditions, any set of primal or dual variables satisfying the Karush-Kuhn-Tucker (KKT) conditions is necessary and suf
Externí odkaz:
http://arxiv.org/abs/2410.15973
The spiking neural networks (SNNs) that efficiently encode temporal sequences have shown great potential in extracting audio-visual joint feature representations. However, coupling SNNs (binary spike sequences) with transformers (float-point sequence
Externí odkaz:
http://arxiv.org/abs/2407.08130
Emphasis in the tensor literature on random embeddings (tools for low-distortion dimension reduction) for the canonical polyadic (CP) tensor decomposition has left analogous results for the more expressive Tucker decomposition comparatively lacking.
Externí odkaz:
http://arxiv.org/abs/2406.09387
Autor:
Bucci, Alberto, Hashemi, Behnam
We present a sequential version of the multilinear Nystr\"om algorithm which is suitable for the low-rank Tucker approximation of tensors given in a streaming format. Accessing the tensor $\mathcal{A}$ exclusively through random sketches of the origi
Externí odkaz:
http://arxiv.org/abs/2407.03849
In this paper, we present a new adaptive rank approximation technique for computing solutions to the high-dimensional linear kinetic transport equation. The approach we propose is based on a macro-micro decomposition of the kinetic model in which the
Externí odkaz:
http://arxiv.org/abs/2406.19479