Molecular Sparse Representation by a 3D Ellipsoid Radial Basis Function Neural Network via L1 Regularization-Reference-Cited by-同舟云学术

Molecular Sparse Representation by a 3D Ellipsoid Radial Basis Function Neural Network via L1 Regularization

Published:2020-11-12 Issue:12 Volume:60 Page:6054-6064
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Gui Sheng¹²³^ORCID,Chen Zhaodi³,Lu Benzhuo¹²^ORCID,Chen Minxin³

Affiliation:

1. State Key Laboratory of Scientific and Engineering Computing, National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China

2. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China

3. Department of Mathematics, Soochow University, Suzhou 215006, China

Funder

Ministry of Science and Technology of the People's Republic of China

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.0c00585

Reference47 articles.

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2. Szegedy, C.; Ioffe, S.; Vanhoucke, V.; Alemi, A. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, arXiv:1602.07261. arXiv.org e-Print archive. https://arxiv.org/abs/1602.07261 (submitted Feb 23, 2016).

3. Krähenbühl, P.; Koltun, V. Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials https://arxiv.org/abs/1210.5644 (submitted Oct 20, 2012).

4. A Novel Continuous Forward Algorithm for RBF Neural Modelling

5. Three learning phases for radial-basis-function networks

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