2015年9月9日 (三) 07:54的版本 （查看源代码） Zxw（讨论 | 贡献） （→‎Speech Processing） ←上一编辑		2015年9月14日 (一) 01:58的版本 （查看源代码） Lr（讨论 | 贡献） （→‎financial group） 下一编辑→
第136行：		第136行：
	==basic rule==		==basic rule==
	* classical tenth model		* classical tenth model
−		+	==multiple-factor==
		+	* add more factor
		+	* use sparse model
	==display==		==display==
	* bug fixed		* bug fixed
−	:* index calculation
	:* buy rule fixed		:* buy rule fixed
−	* document
	==data==		==data==
	* data api		* data api
−	* download data	+	:* download the future data and factor data

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2015年9月14日 (一) 01:58的版本

Speech Processing

AM development

Environment

• grid-12 GPU is transferred to grid-18

RNN AM

• train monophone RNN --zhiyuan

• decode using 5-gram
• the train method of batch

• train using large dataset--mengyuan

• MPE has NAN problem

• write code to tune learning rate--zhiyong

• has completed Nestrov/Adagrad/Adagrad-max
• has unstable phenomenon

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)

• RNN-DAE has worse performance than DNN-DAE because training dataset is small
• extract real room impulse to generate WSJ reverberation data, and then train RNN-DAE

Ivector&Dvector based ASR

• Cluster the speakers to speaker-cluster

• hold

• dark knowledge

• has much worse performance than baseline (EER: base 29% dark knowledge 48%)

• RNN ivector

• hold

• binary ivector done

language vector

• hold
• train using language vector with the dataset of 1400h_CN + 100h_EN--mengyuan
• write a paper--zhiyuan
• RNN language vector

• hold

multi-GPU=

• multi-stream training --Sheng Su

• two GPUs work well, but four GPUs divergent

• solve the problem of buffer--Mengyuan, Sheng Su

Neutral picture style transfer

• reproduced the result of the paper "A neutral algorithm of artistic style" --Zhiyuan, Xuewei
• while subject to the GPU's memory, limited to inception net with sgd optimizer (VGG network with the default L-BFGS optimizer consumes very much memory, which is better)

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

• Test.
• Paper: RNN Rank Net.

• (hold)
• Output rank information.

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.
• Coding.

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

model research

• RNN

• online model, update everyday
• modify cost function and learning method
• add more feature

rule combination

• rule analysis

basic rule

• classical tenth model

multiple-factor

• add more factor
• use sparse model

display

• bug fixed

• buy rule fixed

data

• data api

• download the future data and factor data