2015年8月17日 (一) 08:04的版本 （查看源代码） Zxw（讨论 | 贡献） （→‎Speech Processing） ←上一编辑		2015年8月21日 (五) 06:06的最后版本 （查看源代码） Lr（讨论 | 贡献） （→‎financial group）
第129行：		第129行：
	===strategy===		===strategy===
−	* rule optimize model	+	* rule optimize model (hold)
	:* Adaboost method		:* Adaboost method
	* technology  index		* technology  index
−	:* survey the current technology index	+	:* survey the current technology index and test several type technology index
	* NN		* NN
	:* RNN model using Theano		:* RNN model using Theano
−		+	*
	===display platform===		===display platform===
	* set up the test platform in our grid		* set up the test platform in our grid

---

2015年8月21日 (五) 06:06的最后版本

Speech Processing

AM development

Environment

• grid-14 is on repairation
• prepare to buy a server

RNN AM

• train monophone RNN --zhiyuan
• train using large dataset--mengyuan
• write code to tune learning rate--zhiyong

Mic-Array

• hold
• compute EER with kaldi

====Data selection unsupervised learning

• hold
• acoustic feature based submodular using Pingan dataset --zhiyong
• write code to speed up --zhiyong

RNN-DAE(Deep based Auto-Encode-RNN)

• hold
• deliver to mengyuan, xuewei

• http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=261

Speaker ID

• DNN-based sid --Lantian

• http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=327

Ivector&Dvector based ASR

• hold --Tian Lan
• Cluster the speakers to speaker-classes, then using the distance or the posterior-probability as the metric
• dark-konowlege using i-vector
• train on wsj(testbase dev93+evl92)

• --hold

language vector

• hold
• train using language vector with the dataset of 1400h_CN + 100h_EN--mengyuan

• hold

• write a paper--zhiyuan
• RNN language vector
• train as a paper--xuewei

rectifier

• hold
• rectifier RNN --zhiyuan

• hold

multi-GPU=

• multi-stream training --Sheng Su

Text Processing

RNN LM

• character-lm rnn(hold)
• lstm+rnn

• check the lstm-rnnlm code about how to Initialize and update learning rate.(hold)

Neural Based Document Classification

• (hold)

RNN Rank Task

• Paper: RNN Rank Net.

• (hold)

Graph RNN

• Entity path embeded to entity.

• (hold)

RNN Word Segment

• Set bound to word segment.

• (hold)

Seq to Seq(09-15)

• Review papers.
• Reproduce baseline. (08-03 <--> 08-17)

Order representation

• Nested Dropout

• semi-linear --> neural based auto-encoder.

• modify the objective function(hold)

Balance Representation

• Find error signal

Recommendation

• Reproduce baseline.

• LDA matrix dissovle.
• LDA (Text classification & Recommendation System) --> AAAI

RNN based QA

• Read Source Code.
• Attention based QA.
• (hold)

RNN Poem Process

• Seq based BP.

• (hold)

Text Group Intern Project

Buddhist Process

• (hold)

RNN Poem Process

• Done by Haichao yu & Chaoyuan zuo Mentor : Tianyi Luo.

RNN Document Vector

• (hold)

Image Baseline

• Demo Release.
• Paper Report.

• Read CNN Paper.

Text Intuitive Idea

Trace Learning

• (Hold)

Match RNN

• (Hold)

financial group

tonglian platform

• learn the platform

• arma,ar,boosting tree is done

strategy

• rule optimize model (hold)

• Adaboost method

• technology index

• survey the current technology index and test several type technology index

• NN

• RNN model using Theano

display platform

• set up the test platform in our grid