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Recurrent & convolutional neural network modules

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- - lstm_bptt.py
- - feedforward_net.py
  - logistic_regression.py
- - __init__.py
  - advanced.py
  - basic.py
  - evaluation.py
  - initialization.py
  - optimization.py
- - evaluation.py
  - main.py
  - myio.py
  - README.md
- - api.py
  - evaluation.py
  - main.py
  - myio.py
  - README.md
- - extended_layers.py
  - myio.py
  - options.py
  - rationale.py
  - rationale_dependent.py
  - README.md
- - main.py
  - README.md
- - __init__.py
- __init__.py
- README.md
LICENSE
README.md

// repository documentation

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### Latest updates
Check [code/rationale](/code/rationale) for the project of learning rationales for neural prediction:  <p align="center">
<img height =140 src="code/rationale/figures/example.png"> &nbsp; &nbsp; &nbsp; &nbsp; <img width=350 src="code/rationale/figures/model_framework.png">

[Adam Yala](https://github.com/yala/) has implemented a **Pytorch** version of the rationale project!

Check it out at: https://github.com/yala/text_nn

</p>

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#### About this repo
This repo contains Theano implementations of popular neural network components and optimization methods. Source code of the following papers are also available:

[1] [Molding CNNs for text: non-linear, non-consecutive convolutions](http://arxiv.org/abs/1508.04112). EMNLP 2015
  
  [2] [Semi-supervised Question Retrieval with Gated Convolutions](http://arxiv.org/abs/1512.05726). NAACL 2016

[3] [Rationalizing Neural Predictions](https://people.csail.mit.edu/taolei/papers/emnlp16_rationale.pdf). EMNLP 2016

#### Features
  - Basic modules including feedforward layer, dropout, word embedding, RNN, LSTM, GRU and CNN
  - Optimization methods including SGD, AdaGrad, AdaDelta and Adam
  - Advanced modules from recent papers such as attention and gated convolution.
  - Transparent to use GPU

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#### Projects

###### 1. Neural question retrieval for community-based QA

The directories [code/qa](/code/qa) and [code/pt](/code/pt) contain the implementation of the model described in paper [[2]](http://arxiv.org/abs/1512.05726). Datasets and and pre-trained word vectors are available at [here](https://github.com/taolei87/askubuntu).

###### 2. Sentiment analysis / document classification

The directory [code/sentiment](/code/sentiment) contains the implementation of the model described in paper [[1]](http://arxiv.org/abs/1508.04112). Datasets and and pre-trained word vectors are available at [here](https://github.com/taolei87/text_convnet).

###### 3. Rationalizing neural predictions

The directory [code/rationale](/code/rationale) contains the implementation of the model described in paper [[3]](https://people.csail.mit.edu/taolei/papers/emnlp16_rationale.pdf).

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#### Dependencies
  [Theano](http://deeplearning.net/software/theano/) >= 0.7, Python >= 2.7, Numpy

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