Results 101 to 110 of about 24,295 (253)
Study on 3D Image Reconstruction Model of Sparring Action Based on Graph Neural Network (GNN). [PDF]
Comput Intell Neurosci, 2021 Chang Y, Su M.
europepmc +1 more source
On the equivalence between graph isomorphism testing and function approximation with GNNs
, 2019 Graph neural networks (GNNs) have achieved lots of success on graph-structured data. In the light of this, there has been increasing interest in studying their representation power.
Bruna, Joan +3 more
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Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
Advanced Intelligent Discovery, EarlyView.This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley +1 more source
FL-GNN: Efficient Fusion of Fuzzy Neural Network and Graph Neural Network
Graph Neural Networks (GNNs) have become an important graph feature learning paradigm that is extensively applied to graph inference tasks. However, GNNs still have limitations in some aspects such as representation capability, interpretability, etc.
Boyu Du +3 more
openaire +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez +4 more
wiley +1 more source
When Biology Meets Medicine: A Perspective on Foundation Models
Advanced Intelligent Discovery, EarlyView.Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu +3 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta +3 more
wiley +1 more source
Discover Artificial IntelligenceWith the rapid development of information technology, network engineering education is gradually moving towards intelligence, but existing teaching content analysis and recommendation systems still have limitations in personalized evaluation. Traditional
Lihong Zhao
doaj +1 more source
Graph Element Networks: adaptive, structured computation and memory
, 2019 We explore the use of graph neural networks (GNNs) to model spatial processes in which there is no a priori graphical structure. Similar to finite element analysis, we assign nodes of a GNN to spatial locations and use a computational process defined on ...
Alet, Ferran +5 more
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