Results 61 to 70 of about 145,055 (276)

Structure fusion based on graph convolutional networks for semi-supervised classification

, 2019
Suffering from the multi-view data diversity and complexity for semi-supervised classification, most of existing graph convolutional networks focus on the networks architecture construction or the salient graph structure preservation, and ignore the the ...
Chen, Wanjun, Liao, Kaiyang, Lin, Guangfeng, Wang, Jing, Zhao, Fan   +4 more
core   +1 more source

Linear Graph Convolutional Networks. [PDF]

, 2020
Many neural networks for graphs are based on the graph convolution operator, proposed more than a decade ago. Since then, many alternative definitions have been proposed, that tend to add complexity (and non-linearity) to the model. In this paper, we follow the opposite direction by proposing a linear graph convolution operator. Despite its simplicity,
Navarin N., Erb W., Pasa L., Sperduti A.
openaire   +1 more source

Bioinspired Adaptive Sensors: A Review on Current Developments in Theory and Application

Advanced Materials, EarlyView.
This review comprehensively summarizes the recent progress in the design and fabrication of sensory‐adaptation‐inspired devices and highlights their valuable applications in electronic skin, wearable electronics, and machine vision. The existing challenges and future directions are addressed in aspects such as device performance optimization ...
Guodong Gong, You Zhou, Qingxiu Li, Wenyu Zhao, Sunyingyue Geng, Hangfei Li, Yan‐Bing Leng, Shirui Zhu, Guanglong Ding, Yongbiao Zhai, Ziyu Lv, Ye Zhou, Su‐Ting Han   +12 more
wiley   +1 more source

Analyzing Learned Molecular Representations for Property Prediction

, 2019
Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerprints or expert-
Barzilay, Regina, Coley, Connor, Eiden, Philipp, Gao, Hua, Guzman-Perez, Angel, Hopper, Timothy, Jaakkola, Tommi, Jensen, Klavs, Jin, Wengong, Kelley, Brian, Mathea, Miriam, Palmer, Andrew, Settels, Volker, Swanson, Kyle, Yang, Kevin   +14 more
core   +2 more sources

TOWARDS A SPECTRUM OF GRAPH CONVOLUTIONAL NETWORKS [PDF]

2018 IEEE Data Science Workshop (DSW), 2018
We present our ongoing work on understanding the limitations of graph convolutional networks (GCNs) as well as our work on generalizations of graph convolutions for representing more complex node attribute dependencies. Based on an analysis of GCNs with the help of the corresponding computation graphs, we propose a generalization of existing GCNs where
Mathias Niepert, Alberto García-Durán
openaire   +2 more sources

Artificial Intelligence‐Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling

Advanced Materials, EarlyView.
AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll, Ivan Pinto‐Huguet, Enzo Rotunno, Thomas Galvani, Catalina Coll, Payam Habibzadeh Kavkani, Maria Chiara Spadaro, Yann‐Michel Niquet, Martin Børstad Eriksen, Sara Martí‐Sánchez, Georgios Katsaros, Giordano Scappucci, Peter Krogstrup, Giovanni Isella, Andreu Cabot, Gonzalo Merino, Pablo Ordejón, Stephan Roche, Vincenzo Grillo, Jordi Arbiol   +19 more
wiley   +1 more source

Advanced artificial intelligence in piRNA and PIWI-like protein research: A systematic review of recurrent neural networks, long short-term memory, and emerging computational techniques

Biomédica: revista del Instituto Nacional de Salud
Introduction. PIWI-interacting RNAs are small and non-coding RNAs involved in gene regulation and transposable element repression, emerging as critical biomarkers and therapeutic targets in oncology. Advances in artificial intelligence, such as recurrent
Jheremy Sebastián Reyes, Jhonathan David Guevara, Laura Tatiana Picón, Iris Lorena Sánchez, Libia Andrea Gaona, María Paula Montoya, Luis Eduardo Pino   +6 more
doaj   +1 more source

Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty

, 2019
With the rapid expansion of mobile phone networks in developing countries, large-scale graph machine learning has gained sudden relevance in the study of global poverty. Recent applications range from humanitarian response and poverty estimation to urban
Blumenstock, Joshua E., Khan, Muhammad Raza   +1 more
core   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

Dependency Parsing with Dilated Iterated Graph CNNs

, 2017
Dependency parses are an effective way to inject linguistic knowledge into many downstream tasks, and many practitioners wish to efficiently parse sentences at scale.
McCallum, Andrew, Strubell, Emma
core   +1 more source