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| 第18行： |
第18行： |
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| | ==Work Process== | | ==Work Process== |
| − | ===Similar questions senetence vector model training with RNN/LSTM and the attention RNN/LSTM chatting model training (Tianyi Luo)===
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| − | --------------------2016-04-22
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| − | * Speed up process of the test performance about theano version of Generationg the similar questions' vectors based on RNN.
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| − | --------------------2016-04-21
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| − | * 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.
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| − | * Submit our IJCAI paper to arxiv. (Solve a big problem about submitting the paper including Chinese chacracters. [http://cslt.riit.tsinghua.edu.cn/mediawiki/index.php/How_to_submit_the_latex_files_including_Chinese_characters_to_arxiv Solution])
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| − | * Optimize theano version of Generationg the similar questions' vectors based on RNN.
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| − | --------------------2016-04-20
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| − | * Finish submiting the camera version paper of IJCAI 2016.
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| − | * Update the version of Technical Report about Chinese Song Iambics generation.
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| − | --------------------2016-04-19
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| − | * Optimize theano version of Generationg the similar questions' vectors based on RNN.
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| − | --------------------2016-04-18
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| − | * Optimize theano version of Generationg the similar questions' vectors based on RNN.
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| − | * Finish implementing theano version of LSTM Max margin vector training.
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| − |
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| − | ===Reproduce DSSM Baseline (Chao Xing)===
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| − | : 2016-04-28 : Given a talk to text team for some recently paper.
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| − | Knowledge Base Completion via Search-Based Question Answering : [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/b/b1/Knowledge_Base_Completion_via_Search-Based_Question_Answering_-_Report.pdf pdf]
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| − | Open Domain Question Answering via Semantic Enrichment : [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/1/15/Open_Domain_Question_Answering_via_Semantic_Enrichment_-_Report.pdf pdf]
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| − | A Neural Conversational Model : [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/1/15/A_Neural_Conversational_Model_-_Report.pdf pdf]
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| − | And given a tiny results for CNN-DSSM in huilan's weekly report.
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| − | : 2016-04-27 : Code Multi-layer CNN, suffered from memory error in GPU in tensorflow.
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| − | So I run such test on CPU, should slow.
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| − | : 2016-04-26 : Code done tricky & analysis such tricky.
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| − | : 2016-04-25 : Find a tricky to improve accuracy given by Tianyi.
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| − | : Code for this tricky.
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| − | : 2016-04-23 : Set a series of experiment set.
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| − | 1. Try deep CNN-DSSM, current model just follow proposed model contain one convolution layer, need to be a tuneable parameter.
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| − | 2. Test whether mixture data effective to current model and deep CDSSM.
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| − | 3. Code Recurrent CNN-DSSM (new approach.)
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| − | : 2016-04-22 : Find a problem : Use labs' gpu machine 970 iteration per time is 1537 second but huilan's server is just 7 second.
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| − | Achieve reasonable results when apply max-margin method to CNN-DSSM model.
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| − | : 2016-04-21 : True DSSM model doesn't work well, analysis as below:
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| − | 1. Not exactly reproduce DSSM model, because the original one is English version, I just adapt it to Chinese but after word segmentation.
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| − | So the input is tri-gram words not tri-gram letter.
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| − | 2. Our dataset far from rich, because of we do not use pre-trained word vectors as initial vectors, we can hardly achieve good performance.
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| − | : Request
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| − | 1. As we have rich pre-trained word vectors, maybe CDSSM or RDSSM corrected to our task.
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| − | 2. Different length of sequences seek to be fixed dimension vectors, just CNN and RNN can do such things, DNN can not do it by using
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| − | fix length of word vectors
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| − | : Coding done CDSSM. Test for it's performance.
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| − | One problem : When you install tensorflow by pip 0.8.0 and you want to use conv2d function by gpu, you need make sure you had already
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| − | install your cudnn's version as 4.0 not lastest 5.0.
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| − | : 2016-04-20 : Find reproduced DSSM model's bug, fix it.
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| − | : 2016-04-19 : Code mixture data model by less memory dependency done. Test it's performance.
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| − | : 2016-04-18 : Code mixture data model.
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| − | : 2016-04-16 : Code mixture data model, but face to memory error. Dr. Wang help me fix it.
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| − | : 2016-04-15 : Share Papers. Investigation a series of DSSM papers for future work. And show our intern students how to do research.
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| − | : Original DSSM model : Learning Deep Structured Semantic Models for Web Search using Clickthrough Data [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/4/45/2013_-_Learning_Deep_Structured_Semantic_Models_for_Web_Search_using_Clickthrough_Data_-_Report.pdf pdf]
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| − | : CNN based DSSM model : A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/b/b7/2014_-_A_Latent_Semantic_Model_with_Convolutional-Pooling_Structure_for_Information_Retrieval_-_Report.pdf pdf]
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| − | : Use DSSM model for a new area : Modeling Interestingness with Deep Neural Networks [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/1/1f/2014_-_Modeling_Interestingness_with_Deep_Neural_Networks_-_Report.pdf pdf]
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| − | : Latest approach for LSTM + RNN DSSM model : SEMANTIC MODELLING WITH LONG-SHORT-TERM MEMORY FOR INFORMATION RETRIEVAL [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/2/24/2015_-_SEMANTIC_MODELLING_WITH_LONG-SHORT-TERM_MEMORY_FOR_INFORMATION_RETRIEVAL_-_Report.pdf pdf]
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| − | : 2016-04-14 : Test dssm-dnn model, code dssm-cnn model.
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| − | Continue investigate deep neural question answering system.
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| − | : 2016-04-13 : test dssm model, investigate deep neural question answering system.
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| − | : Share theano ppt [http://cslt.riit.tsinghua.edu.cn/mediawiki/index.php/%E6%96%87%E4%BB%B6:Theano-RBM.pptx theano]
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| − | : Share tensorflow ppt [http://cslt.riit.tsinghua.edu.cn/mediawiki/index.php/%E6%96%87%E4%BB%B6:Tensorflow.pptx tensorflow]
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| − | : 2016-04-12 : Write done dssm tensor flow version.
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| − | : 2016-04-11 : Write tensorflow toolkit ppt for intern student.
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| − | : 2016-04-10 : Learn tensorflow toolkit.
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| − | : 2016-04-09 : Learn tensorflow toolkit.
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| − | : 2016-04-08 : Finish theano version.
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| − | ===Deep Poem Processing With Image (Ziwei Bai)===
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| − | : 2016-04-20 :combine my program with Qixin Wang's
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| − | : 2016-04-10 : web spider to catch a thousand pices of images.
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| − | : 2016-04-13 :1、download theano for python2.7。 2.debug cnn.py
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| − | : 2016-04-15 :web spider to catch 30 thousands pices of images and store them into a matrix
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| − | : 2016-04-16 :modify the code of CNN and spider
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| − | : 2016-04-17 :train convouloutional neural network
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| − |
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| − | ===RNN Piano Processing (Jiyuan Zhang)===
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| − | :2016-4-12：select appropriate midis and run rnnrbm model
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| − | :2016-4-13：view rnnrbm model‘s code
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| − | :2016-4-14~15:coding to select 4/4 beat of midis
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| − | :2016-4-17~22:run data, failed several times ，then modify code and view rnnrbm model's code
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| − | :2016-4-25~29:replace rnnrbm with lstmrbm, then run lstmrbm's model
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| | ===Question & Answering (Aiting Liu)=== | | ===Question & Answering (Aiting Liu)=== |
| | : 2016-04-24 : make my biweekly report | | : 2016-04-24 : make my biweekly report |
| 第105行： |
第25行： |
| | : 2016-04-16 : try to figure out how the PARALAX dataset is constructed | | : 2016-04-16 : try to figure out how the PARALAX dataset is constructed |
| | : 2016-04-17 : download the PARALAX dataset and try to turn it into what we want it to be | | : 2016-04-17 : download the PARALAX dataset and try to turn it into what we want it to be |
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| − | ===Generation Model (Aodong li)===
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| − | : 2016-05-05 : check in
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