Graph stochastic neural networks for semi-supervised learning
, 2020 Graph Neural Networks (GNNs) have achieved remarkable performance in the task of the semi-supervised node classification. However, most existing models learn a deterministic classification function, which lack sufficient flexibility to explore better ...
Wang, H +5 more
core
Attributed Graph Classification via Deep Graph Convolutional Neural Networks [PDF]
, 2019 From social networks to biological networks, graphs are a natural way to represent a diverse set of real-world data. This research presents attributed graph convolutional neural network with a pooling layer (AGCP for short), a novel end-to-end deep ...
Suresh, Susha Pozhampallan
core +1 more source
Super High-Throughput Screening of Enzyme Variants by Spectral Graph Convolutional Neural Networks. [PDF]
J Chem Theory Comput, 2023 Ramírez-Palacios C, Marrink SJ.
europepmc +1 more source
NeurstrucEnergy: A bi-directional GNN model for energy prediction of neural networks in IoT
Digital Communications and NetworksA significant demand rises for energy-efficient deep neural networks to support power-limited embedding devices with successful deep learning applications in IoT and edge computing fields.
Chaopeng Guo +3 more
doaj +1 more source
On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification
Advanced Electronic Materials, EarlyView.ABSTRACT Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memristor‐based circuits are particularly promising for RC, as their intrinsic dynamics can reduce network size and parameter overhead in tasks such as time‐series ...
Rishona Daniels +4 more
wiley +1 more source
Application of Graph Convolutional Neural Networks Combined with Single-Model Decision-Making Fusion Neural Networks in Structural Damage Recognition. [PDF]
Sensors (Basel), 2023 Li X, Xu L, Guo H, Yang L.
europepmc +1 more source
Artificial Intelligence for Fluorite Ferroelectric Materials: From Discovery to Optimization
Advanced Electronic Materials, EarlyView.Artificial intelligence accelerates the discovery and optimization of HfO2‐based fluorite ferroelectrics by linking synthesis, structure, properties, and device performance. Machine learning, deep‐learning analysis, and AI‐driven atomistic modeling enable predictive design, dopant screening, and closed‐loop optimization toward next‐generation ...
Faizan Ali +3 more
wiley +1 more source
Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories
Advanced Energy Materials, EarlyView.In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen +4 more
wiley +1 more source
HiC-GNN: A generalizable model for 3D chromosome reconstruction using graph convolutional neural networks. [PDF]
Comput Struct Biotechnol J, 2023 Hovenga V, Kalita J, Oluwadare O.
europepmc +1 more source
Research on a multimodal emotion perception model based on GCN+GIN hybrid model
Discover Applied SciencesGraph neural networks (GNNs) have demonstrated strong performance in handling graph-structured data in recent years, particularly in capturing complex inter-node relationships among data samples, showcasing advantages over traditional neural networks ...
Yingqiang Wang, Elcid A. Serrano
doaj +1 more source