Bayesian Beta-Bernoulli Process Sparse Coding with Deep Neural NetworksSeveral approximate inference methods have been proposed for deep discrete
latent variable models. However, non-parametric methods which have previously
been successfully employed for classical sparse coding models have largely been
unexplored in the context of deep models. We propose a non-parametric iterative
algorithm for learning discrete latent representations in such deep models.
Additionally, to learn scale invariant discrete features, we propose local data
scaling variables. Lastly, to encourage sparsity in our representations, we
propose a Beta-Bernoulli process prior on the latent factors. We evaluate our
spare coding model coupled with different likelihood models. We evaluate our
method across datasets with varying characteristics and compare our results to
current amortized approximate inference methods.
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