scholarly journals Graph convolutional neural networks with global attention for improved materials property prediction

2020 ◽  
Vol 22 (32) ◽  
pp. 18141-18148
Author(s):  
Steph-Yves Louis ◽  
Yong Zhao ◽  
Alireza Nasiri ◽  
Xiran Wang ◽  
Yuqi Song ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Property Prediction ◽  
Graph Neural Networks

Graph neural networks with local and global attention mechanisms help to extract better features for materials property prediction.

scholarly journals A compact review of molecular property prediction with graph neural networks

2020 ◽  
Author(s):  
Oliver Wieder ◽  
Stefan Kohlbacher ◽  
Mélaine Kuenemann ◽  
Arthur Garon ◽  
Pierre Ducrot ◽  
...  
Keyword(s):  
Neural Networks ◽  
Molecular Property ◽  
Property Prediction ◽  
Graph Neural Networks

scholarly journals Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction

2021 ◽  
Author(s):  
Agnieszka Pocha ◽  
Tomasz Danel ◽  
Sabina Podlewska ◽  
Jacek Tabor ◽  
Lukasz Maziarka
Keyword(s):  
Neural Networks ◽  
Molecular Property ◽  
Property Prediction ◽  
Graph Neural Networks

De Novo Drug Property Prediction using Graph Convolutional Neural Networks

2021 ◽  
Author(s):  
Fairuz Shadmani Shishir ◽  
Khan Md. Hasib ◽  
Shadman Sakib ◽  
Shithi Maitra ◽  
Faisal Muhammad Shah
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
De Novo ◽  
Property Prediction

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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