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TensorFlow Implementation of Neural Variational Inference for Text Processing

• danso 11y ago
  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#issuecomment-162710214 https://github.com/tensorflow/tensorflow/issues/1#issuecomme...
• daenz 11y ago
  Can we get a layperson's explanation of the use-cases or applicability?
• mrdrozdov 11y ago
  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