
TensorFlow Implementation of Neural Variational Inference for Text Processing - guifortaine
https://github.com/carpedm20/variational-text-tensorflow
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mrdrozdov
Here's the abstract from the paper:

Highly expressive directed latent variable models, such as sigmoid belief
networks, are difficult to train on large datasets because exact inference in
them is intractable and none of the approximate inference methods that have
been applied to them scale well. We propose a fast non-iterative approximate
inference method that uses a feedforward network to implement efficient exact
sampling from the variational posterior. The model and this inference network
are trained jointly by maximizing a variational lower bound on the log-
likelihood. Although the naive estimator of the inference model gradient is
too high-variance to be useful, we make it practical by applying several
straightforward model-independent variance reduction techniques. Applying our
approach to training sigmoid belief networks and deep autoregressive networks,
we show that it outperforms the wake-sleep algorithm on MNIST and achieves
state-of-the-art results on the Reuters RCV1 document dataset.

[http://arxiv.org/abs/1402.0030](http://arxiv.org/abs/1402.0030)

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danso
Really cool to see this near the top of the README:

    
    
            Prerequisites
    
            Python 2.7 or Python 3.3+
            NLTK
            TensorFlow
    
    

I remember when TensorFlow was first released that Python 3.x wasn't yet
supported...but searching the archives, it seems that was quickly remedied:

[https://github.com/tensorflow/tensorflow/issues/1#issuecomme...](https://github.com/tensorflow/tensorflow/issues/1#issuecomment-162710214)

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daenz
Can we get a layperson's explanation of the use-cases or applicability?

