Difference between revisions of "NLP Status Report 2016-08-29"

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*revise on the first version
 
*revise on the first version
 
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|Aiting Liu || || *prepare diagrams in chapter3
 
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|Shiyao Li ||
 
|Shiyao Li ||

Revision as of 08:53, 29 August 2016

Date People Last Week This Week
2016/08/22 Yang Feng
  • surveyed the line of neural parsing work
  • arranged the nlp wiki pages
  • arranged the report of tensorflow
  • start the work of neural grammar
Jiyuan Zhang
  • added punctuation to input
  • added input vector to the attention layer
  • the result of the poem7_49k [here]
  • writing books
  • an overview of RNN,RBM,LSTM
Aodong Li
  • Mainly focus on writing chapter 2, Linear Model.
  • Now we have completed Introduction, Polynomial Regression, Linear Regression, Linear Classification, Probabilistic PCA, part of Probabilistic LDA.
  • Complete the remaining of chapter 2--PLDA and Bayesian Approach.
Andy Zhang
  • read books, papers, etc. about chapter3 neural networks;
  • wrote the outine of this chapter
  • start writing my chapter
Aiting Liu
Shiyao Li
2016/08/29 Yang Feng
  • continued surveying the line of neural parsing work
  • read papers of variants of RBM and neural Turing machine
  • learned tensorflow
  • read more papers of Turing machine and get the full picture of my idea
  • start the baseline work of neural Turing machine
Jiyuan Zhang
  • shared an overview of LSTM,RNN,RBM
  • prepared relevant knowledge for writing book
  • ran models of 58k-hybird and 14k-hybird [result]
  • the main focus on writing books
Aodong Li
  • Finish the first version of chapter 2, Linear Model
  • Help Lantian for ICASSP
  • Revise the Linear Model chapter (Waiting for teacher Wang's reply)
Andy Zhang
  • almost finish the first version of chapter3
  • hope to finish the first version before Sep 2nd, main challenge is to draw the pictures myself
  • revise on the first version
Aiting Liu *prepare diagrams in chapter3
Shiyao Li
  • Prepare the presentation of Linear Algebra and Probability Theory and Information Theory
  • start to learn tensorflow
  • learn some models by tensorflow