“2016”版本间的差异
来自cslt Wiki
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* [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/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/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== | ==Speaker recognition== | ||
2016年12月1日 (四) 08:14的版本
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
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
