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Found 2 papers, 1 papers with code
Learning to learn generative programs with Memoised Wake-Sleep
We study a class of neuro-symbolic generative models in which neural networks are used both for inference and as priors over symbolic, data-generating programs.
The Variational Homoencoder: Learning to learn high capacity generative models from few examples
However, when this generative model is expressed as a powerful neural network such as a PixelCNN, we show that existing learning techniques typically fail to effectively use latent variables.
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