Discriminative transfer learning via local and global structure preservation

2018 ◽  
Vol 13 (4) ◽  
pp. 753-760 ◽  
Author(s):  
Chao Wang ◽  
Hongya Tuo ◽  
Jiexin Wang ◽  
Lingfeng Qiao
Keyword(s):  
Transfer Learning ◽  
Global Structure ◽  
Structure Preservation

Dual Global Structure Preservation Based Supervised Feature Selection

2020 ◽  
Vol 51 (3) ◽  
pp. 2765-2787
Author(s):  
Qing Ye ◽  
Xiaolong Zhang ◽  
Yaxin Sun
Keyword(s):  
Feature Selection ◽  
Global Structure ◽  
Structure Preservation

scholarly journals Multiset Canonical Correlations Analysis With Global Structure Preservation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 53595-53603
Author(s):  
Hongjie Zhang ◽  
Jinxin Zhang ◽  
Yanwen Liu ◽  
Ling Jing
Keyword(s):  
Global Structure ◽  
Structure Preservation ◽  
Canonical Correlations

Local and Global Structure Preservation for Robust Unsupervised Spectral Feature Selection

2018 ◽  
Vol 30 (3) ◽  
pp. 517-529 ◽  
Author(s):  
Xiaofeng Zhu ◽  
Shichao Zhang ◽  
Rongyao Hu ◽  
Yonghua Zhu ◽  
Jingkuan Song
Keyword(s):  
Feature Selection ◽  
Spectral Feature ◽  
Global Structure ◽  
Structure Preservation ◽  
Spectral Feature Selection

Indices of transfer: Learning can transfer but still be specific

2010 ◽  
Author(s):  
Erica L. Wohldmann ◽  
Alice F. Healy
Keyword(s):  
Transfer Learning

Molecular Generation Targeting Desired Electronic Properties via Deep Generative Models

2019 ◽  
Author(s):  
Qi Yuan ◽  
Alejandro Santana-Bonilla ◽  
Martijn Zwijnenburg ◽  
Kim Jelfs
Keyword(s):  
Neural Network ◽  
Electronic Properties ◽  
Transfer Learning ◽  
Recurrent Neural Network ◽  
Chemical Space ◽  
Generative Models ◽  
Molecular Features ◽  
Donor Acceptor ◽  
Homo Lumo ◽  
Training Sets

<p>The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties.</p>

Learning from a Neighbor: Adapting a Japanese Parser for Korean Through Feature Transfer Learning

2014 ◽  
Author(s):  
Hiroshi Kanayama ◽  
Youngja Park ◽  
Yuta Tsuboi ◽  
Dongmook Yi
Keyword(s):  
Transfer Learning
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