Results 81 to 90 of about 5,865 (183)

Hypergraph Convolutional Network with Multi-perspective Topology Refinement forSkeleton-based Action Recognition [PDF]

Jisuanji kexue
Since the human skeleton is a natural topological structure,graph convolutional networks(GCNs) are widely used for skeleton-based human action recognition.In recent research,skeleton sequences are represented as spatio-temporal graphs and topology graphs
HUANG Qian, SU Xinkai, LI Chang, WU Yirui
doaj   +1 more source

Toward Useful Quantum Kernels

Advanced Quantum Technologies, Volume 8, Issue 12, December 2025.
The hybrid approach to Quantum Supervised Machine Learning is compatible with Noisy Intermediate Scale Quantum (NISQ) devices but hardly useful. Pure quantum kernels requiring fault‐tolerant quantum computers are more promising. Examples are kernels computed by means of the Quantum Fourier Transform (QFT) and kernels defined via the calculation of ...
Massimiliano Incudini, Francesco Martini, Alessandra Di Pierro   +2 more
wiley   +1 more source

Learning Directed Knowledge Using Higher-Ordered Neural Networks: Building a Predictive Framework

Applied Sciences
Most graph learning methods remain limited to undirected, pairwise interactions, restricting their ability to capture the multi-entity and directional relationships common in real-world systems. We propose the Directed Higher-Ordered Neural Network (HONN)
Yousra Moh Ousellam, Bikram Pratim Bhuyan, Rachida Fissoune, Galina Ivanova, Amar Ramdane-Cherif   +4 more
doaj   +1 more source

Wasserstein Hypergraph Neural Network

The ability to model relational information using machine learning has driven advancements across various domains, from medicine to social science. While graph representation learning has become mainstream over the past decade, representing higher-order relationships through hypergraphs is rapidly gaining momentum.
Duta, Iulia, Liò, Pietro
openaire   +2 more sources

EasyHypergraph: an open-source software for fast and memory-saving analysis and learning of higher-order networks

Humanities & Social Sciences Communications
Higher-order relationships exist widely across different disciplines. In the realm of real-world systems, significant interactions involving multiple entities are common.
Bodian Ye, Min Gao, Xiu-Xiu Zhan, Xinlei He, Zi-Ke Zhang, Qingyuan Gong, Xin Wang, Yang Chen   +7 more
doaj   +1 more source

Recent Advances in Hypergraph Neural Networks

Journal of the Operations Research Society of China
Abstract The growing interest in hypergraph neural networks (HGNNs) is driven by their capacity to capture the complex relationships and patterns within hypergraph structured data across various domains, including computer vision, complex networks, and natural language processing.
Mu-Rong Yang, Xin-Jian Xu
openaire   +3 more sources

Provable Bounds for Learning Some Deep Representations

, 2013
We give algorithms with provable guarantees that learn a class of deep nets in the generative model view popularized by Hinton and others. Our generative model is an $n$ node multilayer neural net that has degree at most $n^{\gamma}$ for some $\gamma
Arora, Sanjeev, Bhaskara, Aditya, Ge, Rong, Ma, Tengyu   +3 more
core  

Resource allocation strategy for ultra-dense Internet of things based on graph convolutional neural network

Tongxin xuebao
To address the significant issue of hidden terminal interference that severely impacted resource management in ultra-dense Internet of things (UD-IoT) environments, a deep deterministic gradient-based conflict-free resource allocation strategy using ...
HUANG Jie, LI Xingxing, YANG Fan, DING Ruijie, CAI Jieliang, YAO Fenghang, ZHANG Xin   +6 more
doaj  

Multi-Temporal Dual Polarimetric SAR Crop Classification Based on Spatial Information Comprehensive Utilization

Remote Sensing
Dual polarimetric SAR is capable of reflecting the biophysical and geometrical information of terrain with open access data availability. When it is combined with time-series observations, it can effectively capture the dynamic evolution of scattering ...
Qiang Yin, Yuming Du, Fangfang Li, Yongsheng Zhou, Fan Zhang   +4 more
doaj   +1 more source

Generalization Performance of Hypergraph Neural Networks

Proceedings of the ACM on Web Conference 2025
Hypergraph neural networks have been promising tools for handling learning tasks involving higher-order data, with notable applications in web graphs, such as modeling multi-way hyperlink structures and complex user interactions. Yet, their generalization abilities in theory are less clear to us.
Yifan Wang, Gonzalo R. Arce, Guangmo Tong   +2 more
openaire   +2 more sources