最新目录

面向功能材料属性预测的机器学习方法初探(4)

来源:功能材料与器件学报 【在线投稿】 栏目:期刊导读 时间:2021-04-02
作者:网站采编
关键词:
摘要:[10]Hinton G E, Osindero S, Teh Y W. A Fast Learning Algorithm for Deep Belief Nets[J]. Neural Computation, 2006,(7): 1527-1554. [11]Huang G, Lee H, Learned-miller E. Learning hierarchical representat

[10]Hinton G E, Osindero S, Teh Y W. A Fast Learning Algorithm for Deep Belief Nets[J]. Neural Computation, 2006,(7): 1527-1554.

[11]Huang G, Lee H, Learned-miller E. Learning hierarchical representations for face verification with convolutional deep belief networks [A]. Proceedings of International Conference on Computer Vision and Pattern Recognition[C].2012.

[12]Lu J, Liong V E, Zhou X, et al. Learning compact binary face descriptor for face recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, (10): 2041-2056.

[13]Le Q V, Karpenko A, Ngiam J. ICA with reconstruction cost for efficient overcomplete feature learning [A]. Proceedings of Conference and Workshop on Neural Information Processing Systems[C]. 2011.

[14]Rifai S, Muller X, Glorot X. Contractive auto-encoders: explicit invariance during feature extraction [A]. Proceedings of International Conference on Machine Learning[C]. 2011.

[15]Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks [A]. Proceedings of Conference and Workshop on Neural Information Processing Systems[C]. 2012.

[16]Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition [A]. Proceedings of International Conference on Learning Representation[C]. 2015.

[17]Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift [A]. Proceedings of International Conference on Machine Learning[C]. 2015.

[18]He K, Zhang X, Ren S, et al. Deep residual learning for image recognition [A]. Proceedings of International Conference on Computer Vision and Pattern Recognition[C]. 2016.

[19]王正国,饶含兵,李泽荣.机器学习方法用于选择性环氧化酶-2抑制剂活性预测模型的建立[J].化学研究与应用,2006,(11):1317-1321.

[20]Sun Y, Bai H, Li Z, et al. Machine learning approach for prediction and understanding of glass-Forming ability [J]. Journal Physics and Chemistry Letter, 2017,(14):3434-3439.

[21]Hansen K, Biegler F, Ramakrishnan R,et al. Machine learning predictions of molecular properties: accurate many-body potentials and nonlocality in chemical space [J]. Journal Physics and Chemistry Letter, 2016,(12): 2326-2331.

[22]Lu S, Zhou Q, Ouyang Y,et al. Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning [J]. Nature Communications, 2018,(9):3405.

文章来源:《功能材料与器件学报》 网址: http://www.gnclyqjxb.cn/qikandaodu/2021/0402/656.html



上一篇:壳聚糖基生物医用材料研究新进展
下一篇:从政务新媒体属性视角探究政务微信建设路径以

功能材料与器件学报投稿 | 功能材料与器件学报编辑部| 功能材料与器件学报版面费 | 功能材料与器件学报论文发表 | 功能材料与器件学报最新目录
Copyright © 2019 《功能材料与器件学报》杂志社 版权所有
投稿电话: 投稿邮箱: