Results 21 to 30 of about 6,963,782 (253)

Temporal Multiresolution Graph Learning

IEEE Access, 2021
Estimating time-varying graphs, i.e., a set of graphs in which one graph represents the relationship among nodes in a certain time slot, from observed data is a crucial problem in signal processing, machine learning, and data mining.
Koki Yamada, Yuichi Tanaka
doaj   +1 more source

Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming [PDF]

, 2022
Graph representation learning (GRL) is critical for graph-structured data analysis. However, most of the existing graph neural networks (GNNs) heavily rely on labeling information, which is normally expensive to obtain in the real world.
Li, Ming, Zheng, Yizhen, Li, M, Li, Yuan-Fang, Li, Zhao, Jin, M, Pan, S, Peng, H, Peng, Hao, Li, YF, Jin, Ming, Li, Z, Pan, Shirui, Zheng, Y   +13 more
core   +1 more source

SAGES: Scalable Attributed Graph Embedding With Sampling for Unsupervised Learning

, 2023
Unsupervised graph embedding method generates node embeddings to preserve structural and content features in a graph without human labeling burden. However, most unsupervised graph representation learning methods suffer issues like poor scalability or ...
Wang, Jialin, Qu, Xiaoru, Zhang, Ji, Bai, Jinze, Li, Zhao, Gao, Jun   +5 more
core   +1 more source

Graph-Based Deep Learning for Graphics Classification

, 2018
Graph-based representations are a common way to deal with graphics recognition problems. However, previous works were mainly focused on developing learning-free techniques.
A Dutta (11812487), P Riba (21906830), J Llados (21906827), A Fornes (21906836)   +3 more
core   +6 more sources

DeepWiN: Deep Graph Reinforcement Learning for User-Centric Radio Access Networks Automation [PDF]

, 2023
The future cellular networks are expected to support an increasing number of users with heterogeneous applications, requiring varying network resources. Therefore, the 6G and beyond cellular networks need to be elastic, and user-centric.
Shaukat, Maria
core  

Graph learning based suicidal ideation detection via tree-drawing test

Frontiers in Psychiatry
IntroductionAdolescent suicide is a critical public health concern worldwide, necessitating effective methods for early detection of high suicidal ideation.
Ye Liu, Jiashuo Zheng, Yang Zeng, Fang Luo, Xuetao Tian, Xuetao Tian   +5 more
doaj   +1 more source

Constructing a metadata knowledge graph as an atlas for demystifying AI pipeline optimization

Frontiers in Big Data
The emergence of advanced artificial intelligence (AI) models has driven the development of frameworks and approaches that focus on automating model training and hyperparameter tuning of end-to-end AI pipelines.
Revathy Venkataramanan, Revathy Venkataramanan, Aalap Tripathy, Tarun Kumar, Sergey Serebryakov, Annmary Justine, Arpit Shah, Suparna Bhattacharya, Martin Foltin, Paolo Faraboschi, Kaushik Roy, Amit Sheth   +11 more
doaj   +1 more source

SCDVit: Semantic Change Detection Based on Sam-Vit and Semantic Consistency

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In recent years, change detection has been a hot research topic in remote sensing. Previous research has focused on binary change detection (BCD), limiting its practical applications.
Ming Chen, Wanshou Jiang
doaj   +1 more source

Ideal Graph of a Graph [PDF]

, 2011
In this paper, we introduce ideal graph of a graph and study some of its properties. We characterize connectedness, isomorphism of graphs and coloring property of a graph using ideal graph.
Manoharan, R., Vasuki, R.
core   +1 more source

Quantitative Stock Selection Model Using Graph Learning and a Spatial–Temporal Encoder

Journal of Theoretical and Applied Electronic Commerce Research
In the rapidly evolving domain of finance, quantitative stock selection strategies have gained prominence, driven by the pursuit of maximizing returns while mitigating risks through sophisticated data analysis and algorithmic models.
Tianyi Cao, Xinrui Wan, Huanhuan Wang, Xin Yu, Libo Xu   +4 more
doaj   +1 more source