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Jopyth edited this page Feb 28, 2016 · 35 revisions

Stefan

01.11.
02.11.
  • Read through distance.c in word2vec to understand word2vec binary format
  • Write Python program to read word vectors with Joseph
  • Set up virtual python environment on server
09.11.
  • Basic network, and experiment infrastructure
  • Write glue code connecting sentence files, sliding window and level db creation
12.11.
  • Create databases, run first experiment on server
  • Caffe Multi-class Precision and Recall

Joseph

  • Read https://code.google.com/p/word2vec/
  • Understand distance.c, implement Python program to read word vectors
  • Explore how to use NLTK for POS tagging
  • Write python script for demo show cases
  • Documentation of demo script
  • Net configuration script
  • PlainText parser
  • Unify python paths and working directory
  • Help messages + README for python script folder
  • Generator usage to reduce memory consumption
  • Intermediate presentation
  • Progress for training instance generation and parsing adapted
  • Python path problems fixed
  • Config file
  • Initial LSTM testing
  • Initial paper stub
  • Parameter evaluation script improved and generalized
  • Chart for harmonic mean, comparison of different features
  • Help Ricarda with error fixing for demo model selection
  • Backwards compatibility of config files
  • Convenience download script for downloading all model files needed for demo from server
  • Try batch processing for speed up of demo (unsuccessful - reverted)
  • Initial fusion methods and class structure, evaluation of these done by Stefan
  • Refactoring for improved readability and versatility of Javascript Code
  • Improved and revised readme file with Ricarda
  • Fix Acoustic Model Index Conversion
  • Paper: Worked on introduction, conclusion, demo sections

Tanja

  • Read https://code.google.com/p/word2vec/
  • Raad some papers
  • Write Python script to parse xml and ASR transcript files
  • Write Python script to create basic training instances using a sliding window
  • Write training instances to leveldb script
  • Ensure valid train and test split
  • Caffe Multi-class Precision and Recall
  • Pipeline work
  • Use POS-Tags as features
  • Introduced a flag to turn on/off POS-Tagging
  • Use parameters from config file
  • Refactoring the input parser
  • Web Demo
  • Presentation
  • Debugging our net/architecture/code for issuse regarding our low precision and recall
  • Several trainings to get the baseline
  • Converting xml and txt files into line format. POS Tags can be preprocessed and written to disk.
  • Main program gets only a config file as argument
  • Refactroing of line parser
  • Preprocessing of POS-Tagging: Write data files with POS tags
  • Refactroing of sliding window: Punctuation pos can be at any position
  • Debugging of Word2Vec: Use Float32 instead of Float64
  • Parse ctm files for generating accoustic training instances.
  • Parse pitch and energy files. Create pitch and energy features for the audio model.
  • Include audio model into web demo
  • Implement first basic fusion
  • Major refactoring of web demo backend
  • Implement evaluation of fusion
  • Final presentation
  • Paper

Ricarda

  • Read https://code.google.com/p/word2vec/
  • Read papers to get familiar with Deep Learning
  • Write Python script to parse xml and ASR transcript files
  • Write Python script to create basic training instances using a sliding window
  • Refactor script for creating trainings instances and work on pipeline to create instances
  • Write python script for demo show cases
  • Use POS-Tags as features
  • LineParser implemented
  • Generator usage to reduce memory consumption
  • Script to collect experiment results
  • Web demo model selection
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