.png){kind=icon}
返回
A precise method for RBMs training using phased curricula
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-022-12973-2.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Restricted Boltzmann machines (RBMs) are efficacious undirected neural networks for generating features and reconstructing images. Nevertheless, the classical persistent chain sampling algorithm has the problem of refactoring failure in the early training stage, which significantly limits the feature extraction and application of RBM. In this paper, motivated by the cumulative nature of the curriculum learning, three Phased Gibbs Sampling (PGS) methods are proposed for more efficient feature extraction and reconstruction by training the RBM periodically. Then, to achieve an automatic and exclusive training step, the innovative Improved Dynamic Learning Rate (IDLR) is designed by cooperating with the reconstruction error and the anti-vibration coefficient. Extensive experimental results of MNIST, 20 Newsgroup, Olivetti face, MNORB, and USPS demonstrate the superiority of three PGS-IDLR algorithms in terms of reconstruction error, training time, and classification accuracy. More specifically, the proposed algorithms can improve the classification accuracy by at least 2% and shorten the training time, compared with the state-of-the-art approaches. Moreover, they achieve a better performance in log-likelihood indictor and image reconstruction.
Keyword:
Deep learning
Restricted boltzmann machines
Phased curricula
Improved dynamic learning rate
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
Two New Plasmid Post-segregational Killing Mechanisms for the Implementation of Synthetic Gene Networks in Escherichia coli.png)
iScience
OAAI
iScienceIF0
OAAI分享收藏Revisiting coexisting chromosomal abnormalities in NPM1-mutated AML in light of the revised ELN 2022 classificationBlood
BloodIF0
PREAI
分享收藏Coordination complexes of niobium and tantalum. V. Eight-coordinated di- and triperoxoniobates(V) and -tantalates(V) with some nitrogen and oxygen bidentate ligands铌和钽的配合物。五。具有一些氮和氧双齿配体的八配位的二过氧化物和三过氧化物铌酸盐 (V) 和钽酸盐 (V)
PREAI
分享收藏Learning Bayesian network structures under incremental construction curriculaNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容