“2015”版本间的差异

2016年2月16日 (二) 02:57的版本 （查看源代码） Tangzy（讨论 | 贡献） （以“ *张之勇 2014-12-28 APSIPA paper reading *[http://www.jmlr.org/proceedings/papers/v32/graves14.pdf 刘超2015-03-11 Towards End-to-End S...”为内容创建页面） 下一编辑→

（没有差异）

---

2016年2月16日 (二) 02:57的版本

• 张之勇 2014-12-28 APSIPA paper reading

• 刘超2015-03-11 Towards End-to-End Speech Recognition with Recurrent Neural Networks

• 汤志远2015-3-18 - Building DNN Acoustic Models for Large Vocabulary Speech Recognition

• 林一叶2015-4-1 - CONTRASTIVE AUTO-ENCODER FOR PHONEME RECOGNITION

• 张雪薇2015-4-1 - speech dereverberation using weighted prediction error with laplacian model of the designed signal

• 王东2015-4-1 - Do Deep Nets Really Need to be Deep?

• 王东2015-4-1 - Distilling the Knowledge in a Neural Network

• 殷实2015-4-8 - Neural_Network_Acoustic_Models_with_Superviesed_Hidden_Layers_for_Automatic_Speech_Recognition

• 赵梦原2015-4-8 - Ensemble_Deep_Learning_for_Speech_Recognition

• 曾翔宇2015-3-11 - An evaluation of target speech for a nonaudible murmur enhancement system in noisy enviroment

• 刘超2015-04-14 Real-time one-pass decoding with recurrent neural network language model for speech recognition

• 汤志远 2015-04-29 Reshaping deep neural network for fast decoding by node-pruning

• 张雪薇2015-4-29 SPEECH DEREVERBERATION WITH MULTI-CHANNEL LINEAR PREDICTION AND SPARSE PRIORS FOR THE DESIRED SIGNAL

• 张雪薇2015-4-29 MULTI-CHANNEL LINEAR PREDICTION-BASED SPEECH DEREVERBERATION WITH LOW-RANK POWER SPECTROGRAM APPROXIMATION

• 张之勇2015-5-6 On the importance of initialization and momentum in deep learning.pdf

• 张雪薇2015-5-20 ICASSP_dereveration

• 汤志远2015-5-27 ICASSP selected readings

• 张之勇2015-6-24 2015_Submodular data selection with acoustic and phonetic features for automatic speech recognition.pdf

• 张雪薇2015-7-1 SPEECH DEREVERBERATION USING A LEARNED SPEECH MODEL.pdf

• 汤志远2015-7-8 Deep Recurrent Neural Networks for Acoustic Modelling

• 汤志远2015-7-8 LSTM: A Search Space Odyssey

• 汤志远2015-7-8 Grid Long Short-Term Memory

• 汤志远2015-7-8 Visualizing and Understanding Recurrent Networks

• 赵梦原2015-7-8 Annealed Dropout_trained Maxout Networks for Improved LVCSR

• 赵梦原2015-7-8 Automatic Pronunciation Verification for Speech Recognition

• 张雪薇2015-7-16 2015_Frame-by-frame_language_identification_in_short_utterances_using_deep_neural_networks

• 张雪薇2015-7-16 SEQUENCE_CLASSIFICATION_USING_THE_HIGH-LEVEL_FEATURES_EXTRACTED

• 王东2015-7-22 What is Wrong with Deep Learning (Yann Lecun at CVPR 2015)

• 王东2015-7-22 A tutorial from Bengio and Lecun

• 王东2015-7-22 ICML 2015 reading list

• 王东2015-7-22 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

• 王东2015-7-24 No initial learning rate anymore

• 王东2015-7-29 Learning Transferable Features with Deep Adaptation Networks

• 王东2015-7-29 Natural Gradient Works Efficiently in Learning

• 王东2015-7-29 Practical Recommendations for Gradient-Based Training of Deep Architectures

• 王东2015-7-29 Efficient Backprop
• 张之勇2015-7-31 2015_Batch normalization Accelerating deep network training by reducing internal covariate shift.pdf

• 赵梦原2015-7-29 A time delay neural network architecture for efficient modeling of long temporal contexts.pdf

• 张雪薇2015-8-05 Feature enhancement by deep LSTM networks for ASRin reverberant multisource environments

• 张雪薇2015-8-05 Recurrent Neural Networks for Noise Reduction in Robust ASR

• 汤志远 2015-8-05 Fast and Accurate Recurrent Neural Network Acoustic Models for Speech Recognition

• 苏圣 2015-8-012 CUDA_C_Programming_Guide.pdf

• 苏圣 2015-8-012 CUBLAS_Library.pdf

• 张之勇 2015-08-12 2015_Deep learning with Elastic Averaging SGD.pdf

• 宁可 2015-08-21 2010_Bed-tree: an all-purpose index structure for string similarity search based on edit distance.pdf

• 张之勇 2015-08-21 2015_ADAM-A method for stochastic optimization.pdf

• 汤志远 2015-8-26 Transitive Transfer Learning

• 汤志远 2015-8-26 Supervised Transfer Sparse Coding

• 张之勇 2015-8-26 2015_EESEN-End-to-end speech recognition using deep rnn models and WFST-based decoding.pdf

• 张之勇 2015-8-26 2015_Dither is better than dropout for regularising deep neural networks.pdf

• 赵梦原 2015-9-2 Context Adaptive Deep Neural Networks for Fast Acoustic Model Adaptation.pdf

• 张雪薇2015-9-02 COMBINATION OF TWO-DIMENSIONAL COCHLEOGRAM AND SPECTROGRAM FEATURES FOR DEEP LEARNING-BASED ASR.pdf

• 张雪薇2015-9-02 DEEP RECURRENT REGULARIZATION NEURAL NETWORK FOR SPEECH RECOGNITION.pdf

• 张雪薇2015-9-02 REGULARIZING DNN ACOUSTIC MODELS WITH GAUSSIAN STOCHASTIC NEURONS.pdf

• 李蓝天2015-9-02 Telephony_Text-Prompted_Speaker_Verification_Using_i-vector_Representation.pdf

• 李蓝天2015-9-02 Deep Neural Networks for Cochannel Speaker Identification.pdf

• 汤志远 2015-9-02 A Neural Algorithm of Artistic Style

• 张之勇 2015-09-09 2007_A hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training.pdf

• 曾翔宇 2015-09-09 without gmm.pdf

• 赵梦原 2015-9-16 Parameterised Sigmoid and ReLU Hidden Activation Functions for DNN Acoustic Modelling.pdf

• 张雪薇2015-9-16 An End-to-end Approach to Language Identification in Short Utterances using Convolutional Neural Networks.pdf

• 汤志远 2015-9-16 2015 Dropout as data augmentation

• 汤志远 2015-9-16 Noise Embeddings for Robust Speech Recognition

• 张之勇 2015-9-23 2015_End-to-End attention-based large vocabulary speech recognition.pdf

• 汤志远 2015-10-22 Interspeech15

• 李蓝天 2015-11-05 Interspeech15-DNNs in speaker recognition.rar

• 汤志远 2015-11-12 Something about RNN or LSTM

• 李蓝天 2015-11-05 PLDA and Spoofing detection

• 汤志远 2015-12-03 Notes for Networks of Memory

• 李蓝天 2015-12-10 D-vector APSIPA 2015

• 张雪薇 2015-12-31 PARALLEL TRAINING OF DNNS WITH NATURAL GRADIENT

• 张雪薇 2015-12-31 PARALLEL poster

• 张雪薇 2015-12-31 TIME DELAY DEEP NEURAL NETWORK-BASED UNIVERSAL BACKGROUND MODELS

• 张雪薇 2015-12-31 A time delay neural network architecture for efficient modeling of long temporal contexts

• 张雪薇 2015-12-31 Semi-supervised Maximum Mutual Information Training of Deep Neural Network Acoustic Models

• 汤志远 2015-12-31 Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

• 汤志远 2015-01-08 Several Tricks for ASR

• 李蓝天 2015-01-14 DL for speaker recognition