Results 51 to 60 of about 537,310 (265)

WMGCN: Weighted Meta-Graph Based Graph Convolutional Networks for Representation Learning in Heterogeneous Networks

IEEE Access, 2020
Network embedding has been an effective tool to analyze heterogeneous networks (HNs) by representing nodes in a low-dimensional space. Although many recent methods have been proposed for representation learning of HNs, there is still much room for ...
Jinli Zhang, Zongli Jiang, Zheng Chen, Xiaohua Hu   +3 more
doaj   +1 more source

Zero Shot Learning with the Isoperimetric Loss

, 2019
We introduce the isoperimetric loss as a regularization criterion for learning the map from a visual representation to a semantic embedding, to be used to transfer knowledge to unknown classes in a zero-shot learning setting.
Bertozzi, Andrea, Deutsch, Shay, Soatto, Stefano   +2 more
core   +1 more source

Graph Representations for Reinforcement Learning

Journal of Computer Science and Technology
Graph analysis is becoming increasingly important due to the expressive power of graph models and the efficient algorithms available for processing them. Reinforcement Learning is one domain that could benefit from advancements in graph analysis, given that a learning agent may be integrated into an environment that can be represented as a graph ...
Schab, Esteban, Casanova, Carlos, Piccoli, Fabiana   +2 more
openaire   +3 more sources

RNA Sequencing Resolves Cryptic Pathogenic Variants in Mitochondrial Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Mitochondrial diseases are the most common inherited metabolic disorders, characterized by pronounced clinical and genetic heterogeneity that complicates molecular diagnosis. Although DNA‐based sequencing approaches have become standard in genetic testing, up to half of patients remain without a definitive diagnosis.
Zhimei Liu, Xin Duan, Fatemeh Peymani, Jia Wang, Chengjia Bao, Chaolong Xu, Ying Zou, Zixuan Zhang, Yunxi Zhang, Tongyue Li, Martin Pavlov, Junling Wang, Minhan Song, Tianyu Song, Xiaodi Han, Mingxi Sun, Danmin Shen, Ruoyu Duan, Huafang Jiang, Manting Xu, Holger Prokisch, Fang Fang   +21 more
wiley   +1 more source

Visual evaluation of graph representation learning based on the presentation of community structures

Visual Informatics
Various graph representation learning models convert graph nodes into vectors using techniques like matrix factorization, random walk, and deep learning. However, choosing the right method for different tasks can be challenging.
Yong Zhang, Lihong Cai, Yuhua Liu, Yize Li, Songyue Li, Yuming Ma, Yuwei Meng, Zhiguang Zhou   +7 more
doaj   +1 more source

Learning Robust Representation Through Graph Adversarial Contrastive Learning

, 2022
Existing studies show that node representations generated by graph neural networks (GNNs) are vulnerable to adversarial attacks, such as unnoticeable perturbations of adjacent matrix and node features. Thus, it is requisite to learn robust representations in graph neural networks. To improve the robustness of graph representation learning, we propose a
Guo, Jiayan, Li, Shangyang, Zhao, Yue, Zhang, Yan   +3 more
openaire   +2 more sources

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

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

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

gat2vec: representation learning for attributed graphs

Computing, 2018
Network representation learning (NRL) enables the application of machine learning tasks such as classification, prediction and recommendation to networks. Apart from their graph structure, networks are often associated with diverse information in the form of attributes.
Nasrullah Sheikh, Zekarias Kefato, Alberto Montresor   +2 more
openaire   +2 more sources