Results 1 to 10 of about 116,308 (181)

Holomorphic subgraph reduction of higher-point modular graph forms

Journal of High Energy Physics, 2019
Modular graph forms are a class of modular covariant functions which appear in the genus-one contribution to the low-energy expansion of closed string scattering amplitudes.
Jan E. Gerken, Justin Kaidi
doaj   +3 more sources

Multi relational dual attention graph transformer for fine grained sentiment analysis [PDF]

Scientific Reports
Aspect-Based Sentiment Analysis requires precise identification of sentiment polarity toward specific aspects, demanding robust modeling of syntactic, semantic, and discourse-level dependencies.
Anusha P. Anilkumar, Soo-Kyun Kim, Yeo-Chan Yoon   +2 more
doaj   +2 more sources

Graph-Enhanced Expectation Maximization for Emission Tomography [PDF]

Journal of Imaging
Emission tomography, including single-photon emission computed tomography (SPECT), requires image reconstruction from noisy and incomplete projection data.
Ryosuke Kasai, Hideki Otsuka
doaj   +2 more sources

Node-Adaptive Regularization for Graph Signal Reconstruction

IEEE Open Journal of Signal Processing, 2021
A critical task in graph signal processing is to estimate the true signal from noisy observations over a subset of nodes, also known as the reconstruction problem.
Maosheng Yang, Mario Coutino, Geert Leus, Elvin Isufi   +3 more
doaj   +1 more source

Neural Networks Regularization With Graph-Based Local Resampling

IEEE Access, 2021
This paper presents the concept of Graph-based Local Resampling of perceptron-like neural networks with random projections (RN-ELM) which aims at regularization of the yielded model.
Alex D. Assis, Luiz C. B. Torres, Lourenco R. G. Araujo, Vitor M. Hanriot, Antonio P. Braga   +4 more
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Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark

Frontiers in Artificial Intelligence, 2022
In this work, we study the phenomenon of catastrophic forgetting in the graph representation learning scenario. The primary objective of the analysis is to understand whether classical continual learning techniques for flat and sequential data have a ...
Antonio Carta, Andrea Cossu, Andrea Cossu, Federico Errica, Davide Bacciu   +4 more
doaj   +1 more source

Hyperspectral Image Super-Resolution Algorithm Based on Graph Regular Tensor Ring Decomposition

Remote Sensing, 2023
This paper introduces a novel hyperspectral image super-resolution algorithm based on graph-regularized tensor ring decomposition aimed at resolving the challenges of hyperspectral image super-resolution.
Shasha Sun, Wenxing Bao, Kewen Qu, Wei Feng, Xiaowu Zhang, Xuan Ma   +5 more
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Probabilistic regular graphs [PDF]

Electronic Proceedings in Theoretical Computer Science, 2010
In Proceedings INFINITY 2010, arXiv:1010 ...
Bertrand, Nathalie, Morvan, Christophe
openaire   +4 more sources

Semisupervised Hyperspectral Image Classification via Superpixel-Based Graph Regularization With Local and Nonlocal Features

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Although a graph-based semisupervised learning (SSL) approach can utilize limited numbers of labeled samples for hyperspectral image (HSI) classification, it is difficult to use the large amount of pixels in an HSI to construct a large-scale graph.
Longshan Yang, Junhuan Peng, Yuebin Wang, Linlin Xu, Weiwei Zhu   +4 more
doaj   +1 more source

PolSAR Image Feature Extraction via Co-Regularized Graph Embedding

Remote Sensing, 2020
Dimensionality reduction (DR) methods based on graph embedding are widely used for feature extraction. For these methods, the weighted graph plays a vital role in the process of DR because it can characterize the data’s structure information.
Xiayuan Huang, Xiangli Nie, Hong Qiao
doaj   +1 more source