Results 71 to 80 of about 3,760 (232)

Enhancing named entity recognition in low-resource scenarios using diffusion variational autoencoder and knowledge graphs

Journal of King Saud University: Computer and Information Sciences
In low-resource Named Entity Recognition (NER) tasks, existing deep learning methods often suffer from inaccurate entity boundary identification and insufficient contextual understanding due to scarce annotated data.
Libo Dong, Yunfei Qiu, Wenwen Zhang
doaj   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Improving Variational Graph Autoencoders With Multi-Order Graph Convolutions

IEEE Access
Variational Graph Autoencoders (VAGE) emerged as powerful graph representation learning methods with promising performance on graph analysis tasks. However, existing methods typically rely on Graph Convolutional Networks (GCN) to encode the attributes and topology of the original graph.
Lining Yuan, Ping Jiang, Zhu Wen, Jionghui Li   +3 more
openaire   +2 more sources

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +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

SAT Instances Generation Using Graph Variational Autoencoders

ESANN 2024 proceesdings
This paper presents a SAT instance generator using a Graph Variational Autoencoder (GVAE2SAT) architecture that outperforms existing generative deep learning models in speed and requires minimal post-processing. Our computational analyses benchmark this model against current deep learning techniques, introducing advanced metrics for more accurate ...
Daniel Crowley, Marco Dalla, Barry O'Sullivan, Andrea Visentin   +3 more
openaire   +2 more sources

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

Advanced Intelligent Discovery, EarlyView.
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath, Shauryaraj K. Jilkar, Aditya Udgaonkar, Yu (Joe) Lu, Ali Mesbah   +4 more
wiley   +1 more source

Transformer graph variational autoencoder for generative molecular design

Biophysical Journal
ABSTRACT In the field of drug discovery, the generation of new molecules with desirable properties remains a critical challenge. Traditional methods often rely on SMILES (Simplified Molecular Input Line Entry System) representations for molecular input data, which can limit the diversity and novelty of generated molecules.
Trieu Nguyen, Aleksandra Karolak
openaire   +2 more sources

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, Yuxing Wang, Limin Zou, Jing Huang   +3 more
wiley   +1 more source

Enhancing Gas Turbine Fault Diagnosis Using a Multi-Scale Dilated Graph Variational Autoencoder Model

IEEE Access
This paper proposes a Multi-scale Dilated Variational Graph Convolutional Autoencoder (MG-VAE) model for gas turbine fault diagnosis. The model integrates a multi-scale dilated convolutional attention mechanism to extract features across different scales,
Zhang Kun, Li Hongren, Wang Xin, Xie Daxing, Yang Shuai   +4 more
doaj   +1 more source