Results 61 to 70 of about 6,810,610 (247)

LIGHTGCL: SIMPLE YET EFFECTIVE GRAPH CONTRASTIVE LEARNING FOR RECOMMENDATION

, 2023
Graph neural network (GNN) is a powerful learning approach for graph-based recommender systems. Recently, GNNs integrated with contrastive learning have shown superior performance in recommendation with their data augmentation schemes, aiming at dealing ...
Cai, Xuheng, Xia, Lianghao, Huang, Chao, Ren, Xubin   +3 more
core  

Generative AI Utilization and Perspectives Among Reproductive‐Age Women with Systemic Autoimmune and Rheumatic Diseases

Arthritis Care &Research, Accepted Article.
Objective Reproductive‐age women with systemic autoimmune and rheumatic diseases (SARDs) have unique information needs related to their SARDs and reproductive health. We sought to understand their use of and receptivity to current and hypothetical generative artificial intelligence (AI) tools for health information‐seeking. Methods We conducted a cross‐
Mariam Arif, Catherine Wright, Samantha Rodriguez, Sarah Graves, Tamar Krishnamurti, Mehret Birru Talabi   +5 more
wiley   +1 more source

CLB-Defense: based on contrastive learning defense for graph neural network against backdoor attack

Tongxin xuebao, 2023
For the problem that the existing backdoor attack defense methods are difficult to deal with irregular and unstructured discrete graph data to alleviate the threat of backdoor attacks, a backdoor attack defense method for GNN based on contrastive ...
Jinyin CHEN, Haiyang XIONG, Haonan MA, Yayu ZHENG   +3 more
doaj   +2 more sources

Contrastive Graph Similarity Networks

Graph similarity learning is a significant and fundamental issue in the theory and analysis of graphs, which has been applied in a variety of fields, including object tracking, recommender systems, similarity search, and so on.
Li, Fuyi, Jin, D., Zheng, Yizhen, Jin, Di, Li, F., Pan, S., Pan, Shirui, Qiao, Yongliang, Zheng, Y., Wang, Luzhi, Qiao, Y., Wang, L.   +11 more
core   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

Learning from Feature and Global Topologies: Adaptive Multi-View Parallel Graph Contrastive Learning

Mathematics
To address the limitations of existing graph contrastive learning methods, which fail to adaptively integrate feature and topological information and struggle to efficiently capture multi-hop information, we propose an adaptive multi-view parallel graph ...
Yumeng Song, Xiaohua Li, Fangfang Li, Ge Yu   +3 more
doaj   +1 more source

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

Multi-View Graph Contrastive Neural Networks for Session-Based Recommendation

Mathematics
Session-based recommendation (SBR) aims to predict the next item a user may interact with based on an anonymous session, playing a crucial role in real-time recommendation scenarios.
Pengbo Huang, Chun Wang
doaj   +1 more source

Thermodynamic Pathways of Nonequilibrium Solidification in Wire‐Arc Additive Manufacturing Fe‐Based Multicomponent Alloy Structures

Advanced Engineering Materials, EarlyView.
Geometry‐driven thermal behavior in wire‐arc additive manufacturing (WAAM) influences microstructural evolution during nonequilibrium solidification of a chemically complex Fe–Cr–Nb–W–Mo–C nanocomposite system. By comparing different deposits configurations, distinct entropy–cooling rate correlations, segregation, and carbide evolution are revealed ...
Blanca Palacios, Anthony Gonzalez, Aleksander Aleman, Tyler Dolmetsch, Tanaji Paul, Arvind Agarwal   +5 more
wiley   +1 more source

Self-Supervised Heterogeneous Graph Neural Network with Multi-scale Meta-Path Contrastive Learning

International Journal of Computational Intelligence Systems
Heterogeneous graph neural networks (HGNNs) have showcased exceptional modeling prowess in characterizing intricate structures and diverse semantic information.
Yufei Wu, Xiumei Wen, Fanxing Meng, Yingxue Mu   +3 more
doaj   +1 more source