Schedule

Text Processing Team Schedule

Members

Former Members

• Rong Liu (刘荣) : 优酷
• Xiaoxi Wang (王晓曦) : 图灵机器人
• Xi Ma (马习) : 清华大学研究生
• DongXu Zhang (张东旭) : --

Current Members

• Tianyi Luo (骆天一)
• Chao Xing (邢超)
• Qixin Wang (王琪鑫)
• Yiqiao Pan (潘一桥)

Work Process

Similar questions senetence vector training with RNN/LSTM(Tianyi Luo)

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2016-04-18

• Optimize theano version of Generationg the similar questions' vectors based on RNN.
• Finish implementing theano version of LSTM Max margin vector training.

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2016-04-19

• Optimize theano version of Generationg the similar questions' vectors based on RNN.

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2016-04-20

• Finish submiting the camera version paper of IJCAI 2016.
• Update the version of Technical Report about Chinese Song Iambics generation.

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2016-04-21

• Finish helping Teacher Wang to prepare for text group's presentation(Tang poetry and Songci generation and Intelligent QA system) for Tsinghua University's 105 anniversary.
• Submit our IJCAI paper to arxiv. (Solve a big problem about submitting the paper including Chinese chacracters)
• Optimize theano version of Generationg the similar questions' vectors based on RNN.

Reproduce DSSM Baseline (Chao Xing)

2016-04-20 : Find reproduced DSSM model's bug, fix it.

2016-04-19 : Code mixture data model by less memory dependency done. Test it's performance.

2016-04-18 : Code mixture data model.

2016-04-16 : Code mixture data model, but face to memory error. Dr. Wang help me fix it.

2016-04-15 : Share Papers. Investigation a series of DSSM papers for future work. And show our intern students how to do research.

            : Original DSSM model : Learning Deep Structured Semantic Models for Web Search using Clickthrough Data pdf
            : CNN based DSSM model : A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval pdf
            : Use DSSM model for a new area : Modeling Interestingness with Deep Neural Networks pdf
            : Latest approach for LSTM + RNN DSSM model : SEMANTIC MODELLING WITH LONG-SHORT-TERM MEMORY FOR INFORMATION RETRIEVAL pdf

2016-04-14 : Test dssm-dnn model, code dssm-cnn model.

              Continue investigate deep neural question answering system.

2016-04-13 : test dssm model, investigate deep neural question answering system.

            : Share theano ppt theano
            : Share tensorflow ppt tensorflow

2016-04-12 : Write done dssm tensor flow version.

2016-04-11 : Write tensorflow toolkit ppt for intern student.

2016-04-10 : Learn tensorflow toolkit.

2016-04-09 : Learn tensorflow toolkit.

2016-04-08 : Finish theano version.

Deep Poem Processing With Image (Ziwei Bai)

2016-04-20 :combine my program with Qixin Wang's

2016-04-10 : web spider to catch a thousand pices of images.

2016-04-13 :1、download theano for python2.7。 2.debug cnn.py

2016-04-15 :web spider to catch 30 thousands pices of images and store them into a matrix

2016-04-16 :modify the code of CNN and spider

2016-04-17 :train convouloutional neural network

RNN Music Processing for lyric (Shiyao Li)

2016-04-20 : learn LSTM

2016-04-09 : web spider to catch a thousand pieces of lyrics.

2016-04-10 : extract the keywords in the lyrics

2016-04-13 :Read paper Memory Network.

2016-04-15 :read the paper Memory Network and start to understand its code

2016-04-17 :read paper end to end memory network

RNN Key word Poem Processing (Yi Xiong)

2016-04-20 : learn web spider

2016-04-09 : Database for N-Gram data storing

2016-04-10 : dictionary stored in database , dictionary based segmentation and a simple bigram segmentation

2016-04-13 : segmentation result analysis

2016-04-15 :improve the simple bigram segmentation

2016-04-16 :compare the result of bigram segmentation with dictionary segmentation

2016-04-17 :learn python (head first 50%)

RNN Piano Processing (Jiyuan Zhang)

2016-4-12：select appropriate midis and run rnnrbm model

2016-4-13：view rnnrbm model‘s code

Recommendation System (Tong Liu)

2016-04-09 : 1.read a review:Machine learning:Trends,perspectives, and prospects 2.learn python ,can operate dict and set

2016-04-12 : 1.read paper Collaborative Deep Learning for Recommender Systems and take notes.2. learn the concepts of stacked denoising autoencoder(SDAE).

2016-04-17 :1.allocate PuTTy and Xming 2.learn python, can operate slice and iterator 3.learn release and datasets of a paper: Collaborative Deep Learning for Recommender Systems

Question & Answering (Aiting Liu)

2016-04-20 : read Fader's paper ()2013

2016-04-15 :learn dssm and sent2vec

2016-04-16 :try to figure out how thePARALAX dataset is constructed

2016-04-17 :download the PARALAX dataset and turn it into what we want it to be