Browsing by Author "Huang, Mou-Yue"

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FAST AND ACCURATE IMAGE RECOGNITION USING DEEPLY-FUSED BRANCHY NETWORKS
(2017-01-01)
Huang, Mou-Yue
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Lai, Ching-Hao
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Chen, Sin-Horng
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電機工程學系
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Department of Electrical and Computer Engineering
In order to achieve higher accuracy of image recognition, deeper and wider networks have been used. However, when the network size gets bigger, its forward inference time also takes longer. To address this problem, we propose Deeply Fused Branchy Network (DFB-Net) by adding small but complete side branches to the target baseline main branch. DFB-Net allows easy-to-discriminate samples to be classified faster. For hard-to-discriminate samples, DFB-Net makes probability fusion by averaging softmax probabilities to make collaborative predictions. Extensive experiments on the two CIFAR datasets show that DFB-Net achieves state-ofthe-art results to obtain an error rate of 3.07% on CIFAR-10 and 16.01% on CIFAR-100. Meanwhile, the forward inference time (with a batch size of 1 and averaged among all test samples) only takes 10.4 ms on CIFAR-10, 18.8 ms on CIFAR-100, using GTX 1080 GPU with cuDNN 5.1.
Rapid Bayesian Learning for Recurrent Neural Network Language Model
(2014-01-01)
Chien, Jen-Tzung
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Ku, Yuan-Chu
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Huang, Mou-Yue
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電機資訊學士班
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Undergraduate Honors Program of Electrical Engineering and Computer Science
This paper presents Bayesian learning for recurrent neural network language model (RNN-LM). Our goal is to regularize the RNN-LM by compensating for the randomness of the estimated model parameters which is characterized by a Gaussian prior. This model is not only constructed by training the synaptic weight parameters according to the maximum a posteriori criterion but also regularized by estimating the Gaussian hyperparameter through the type 2 maximum likelihood. However, a critical issue in Bayesian RNN-LM is the heavy computation of Hessian matrix which is formed as the sum of a large amount of outer-products of high-dimensional gradient vectors. We present a rapid approximation to reduce the redundancy due to the curse of dimensionality and speed up the calculation by summing up only the salient outer-products. Experiments on 1B-Word Benchmark, Penn Treebank and World Street Journal corpora show that rapid Bayesian RNN-LM consistently improves the perplexity and word error rate in comparison with standard RNN-LM.