“2016”版本间的差异
来自cslt Wiki
（→DNN architecture） |
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| （3位用户的8个中间修订版本未显示） | |||
| 第1行： | 第1行： | ||
| − | DNN architecture | + | ==DNN architecture== |
| − | [http://www.isca-speech.org/archive/Interspeech_2016/pdfs/1446.pdf Ying Zhang et al. Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks] | + | * [http://www.isca-speech.org/archive/Interspeech_2016/pdfs/1446.pdf Ying Zhang et al. Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks] |
| + | * [[媒体文件:OUTRAGEOUSLYLARGENEURALNETWORKSTHESPARSELY-GATEDMIXTURE-OF-EXPERTSLAYER.pdf|ICLR2017: OUTRAGEOUSLY LARGE NEURAL NETWORKS: THE SPARSELY-GATED MIXTURE-OF-EXPERTS LAYER]] | ||
| + | * [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/f/fb/LightRNN.pdf lightRNN from microsoft] | ||
| + | * [https://arxiv.org/pdf/1512.03385v1.pdf Kaiming He et al. Deep Residual Learning for Image Recognition] | ||
| + | * [http://www.isca-speech.org/archive/Interspeech_2016/pdfs/0515.pdf Wei-Ning Hsu et al. Exploiting Depth and Highway Connections in Convolutional Recurrent Deep Neural Networks for Speech Recognition] | ||
| + | * [http://t.cn/RfZHxko MICRO 2016 ] | ||
| + | * [[媒体文件:Cambricon-X.pdf| Cambricon-X: An Accelerator for Sparse Neural Networks]] | ||
| + | * [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/2/26/REVISE_SATURATED_ACTIVATION_FUNCTIONS.pdf revise saturated activation functions] | ||
| − | + | ==Visualization== | |
| − | [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/f/ | + | * [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/b/b9/Visualizing_and_Understanding_Genomic.pdf Visualizing and Understanding Genomic Sequences Using Deep Neural Networks] |
| + | * [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/4/43/On_the_Role_of_Nonlinear_Transformations_in_Deep_Neural_Network_Acoustic_Models.PDF On the Role of Nonlinear Transformations in Deep Neural Network Acoustic Models] | ||
| + | * [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/f/f6/Understanding_intermediate_layers_using_linear_classifier_probes.pdf Understanding_intermediate_layers_using_linear_classifier_probes] | ||
| − | + | ==Speaker recognition== | |
| − | [http:// | + | * [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/1/1b/RedDots.rar# INTERSPEECH 2016 Fri-O-2-2 :Special Session: The RedDots Challenge: Towards Characterizing Speakers from Short Utterances] |
| + | * [http://192.168.0.51:8888/2016/interspeech2016/WELCOME.html# INTERSPEECH 2016 Fri-O-3-2 : Special Session: The Speakers in the Wild (SITW) Speaker Recognition Challenge] | ||
| − | + | ==Review== | |
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*[[媒体文件:Note icassp16.pdf|Zhiyuan Tang 20160520 - ICASSP 2016 summary ]] | *[[媒体文件:Note icassp16.pdf|Zhiyuan Tang 20160520 - ICASSP 2016 summary ]] | ||
*[[媒体文件:Nn analysis.pdf |Zhiyuan Tang 20160802 - Visualizing, Measuring and Understanding Neural Networks: A Brief Survey ]] | *[[媒体文件:Nn analysis.pdf |Zhiyuan Tang 20160802 - Visualizing, Measuring and Understanding Neural Networks: A Brief Survey ]] | ||
| + | *[[媒体文件:Interspeech16 review.pdf|Zhiyuan Tang 20161122 - INTERSPEECH 2016 summary ]] | ||
2016年12月1日 (四) 08:17的最后版本
DNN architecture
- Ying Zhang et al. Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks
- ICLR2017: OUTRAGEOUSLY LARGE NEURAL NETWORKS: THE SPARSELY-GATED MIXTURE-OF-EXPERTS LAYER
- lightRNN from microsoft
- Kaiming He et al. Deep Residual Learning for Image Recognition
- Wei-Ning Hsu et al. Exploiting Depth and Highway Connections in Convolutional Recurrent Deep Neural Networks for Speech Recognition
- MICRO 2016
- Cambricon-X: An Accelerator for Sparse Neural Networks
- revise saturated activation functions
Visualization
- Visualizing and Understanding Genomic Sequences Using Deep Neural Networks
- On the Role of Nonlinear Transformations in Deep Neural Network Acoustic Models
- Understanding_intermediate_layers_using_linear_classifier_probes
Speaker recognition
- INTERSPEECH 2016 Fri-O-2-2 :Special Session: The RedDots Challenge: Towards Characterizing Speakers from Short Utterances
- INTERSPEECH 2016 Fri-O-3-2 : Special Session: The Speakers in the Wild (SITW) Speaker Recognition Challenge
