“ASR:2015-09-09”版本间的差异

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Speech Processing
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financial group
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

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