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Deep Neural Networks with Multistate Activation Functions.

Cai C, Xu Y, Ke D, Su K - Comput Intell Neurosci (2015)

Bottom Line: Experimental results on the TIMIT corpus reveal that, on speech recognition tasks, DNNs with MSAFs perform better than the conventional DNNs, getting a relative improvement of 5.60% on phoneme error rates.Further experiments also reveal that mean-normalised SGD facilitates the training processes of DNNs with MSAFs, especially when being with large training sets.The models can also be directly trained without pretraining when the training set is sufficiently large, which results in a considerable relative improvement of 5.82% on word error rates.

View Article: PubMed Central - PubMed

Affiliation: School of Technology, Beijing Forestry University, No. 35 Qinghuadong Road, Haidian District, Beijing 100083, China.

ABSTRACT
We propose multistate activation functions (MSAFs) for deep neural networks (DNNs). These MSAFs are new kinds of activation functions which are capable of representing more than two states, including the N-order MSAFs and the symmetrical MSAF. DNNs with these MSAFs can be trained via conventional Stochastic Gradient Descent (SGD) as well as mean-normalised SGD. We also discuss how these MSAFs perform when used to resolve classification problems. Experimental results on the TIMIT corpus reveal that, on speech recognition tasks, DNNs with MSAFs perform better than the conventional DNNs, getting a relative improvement of 5.60% on phoneme error rates. Further experiments also reveal that mean-normalised SGD facilitates the training processes of DNNs with MSAFs, especially when being with large training sets. The models can also be directly trained without pretraining when the training set is sufficiently large, which results in a considerable relative improvement of 5.82% on word error rates.

No MeSH data available.


Related in: MedlinePlus

The cross entropy losses of the models using the symmetrical MSAF on the cross-validation set.
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fig12: The cross entropy losses of the models using the symmetrical MSAF on the cross-validation set.

Mentions: Figure 12 provides curves of the cross entropy losses when the models use the symmetrical MSAF as their activation functions as well as a curve of the logistic function. At the beginning, the symmetrical MSAF does not have better performance than the logistic function. Nevertheless, it performs better when the cross entropy losses drop significantly during the sixth epoch. The symmetrical MSAFs both without and with mean-normalisation see considerably lower cross entropy losses at the end of training, and the one with mean-normalisation performs slightly better than the other.


Deep Neural Networks with Multistate Activation Functions.

Cai C, Xu Y, Ke D, Su K - Comput Intell Neurosci (2015)

The cross entropy losses of the models using the symmetrical MSAF on the cross-validation set.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4581500&req=5

fig12: The cross entropy losses of the models using the symmetrical MSAF on the cross-validation set.
Mentions: Figure 12 provides curves of the cross entropy losses when the models use the symmetrical MSAF as their activation functions as well as a curve of the logistic function. At the beginning, the symmetrical MSAF does not have better performance than the logistic function. Nevertheless, it performs better when the cross entropy losses drop significantly during the sixth epoch. The symmetrical MSAFs both without and with mean-normalisation see considerably lower cross entropy losses at the end of training, and the one with mean-normalisation performs slightly better than the other.

Bottom Line: Experimental results on the TIMIT corpus reveal that, on speech recognition tasks, DNNs with MSAFs perform better than the conventional DNNs, getting a relative improvement of 5.60% on phoneme error rates.Further experiments also reveal that mean-normalised SGD facilitates the training processes of DNNs with MSAFs, especially when being with large training sets.The models can also be directly trained without pretraining when the training set is sufficiently large, which results in a considerable relative improvement of 5.82% on word error rates.

View Article: PubMed Central - PubMed

Affiliation: School of Technology, Beijing Forestry University, No. 35 Qinghuadong Road, Haidian District, Beijing 100083, China.

ABSTRACT
We propose multistate activation functions (MSAFs) for deep neural networks (DNNs). These MSAFs are new kinds of activation functions which are capable of representing more than two states, including the N-order MSAFs and the symmetrical MSAF. DNNs with these MSAFs can be trained via conventional Stochastic Gradient Descent (SGD) as well as mean-normalised SGD. We also discuss how these MSAFs perform when used to resolve classification problems. Experimental results on the TIMIT corpus reveal that, on speech recognition tasks, DNNs with MSAFs perform better than the conventional DNNs, getting a relative improvement of 5.60% on phoneme error rates. Further experiments also reveal that mean-normalised SGD facilitates the training processes of DNNs with MSAFs, especially when being with large training sets. The models can also be directly trained without pretraining when the training set is sufficiently large, which results in a considerable relative improvement of 5.82% on word error rates.

No MeSH data available.


Related in: MedlinePlus