Bayesian Beta-Bernoulli Process Sparse Coding with Deep Neural Networks

• URL: http://arxiv.org/abs/2303.08230v1
• Date: Tue, 14 Mar 2023 20:50:12 GMT
• Title: Bayesian Beta-Bernoulli Process Sparse Coding with Deep Neural Networks
• Authors: Arunesh Mittal, Kai Yang, Paul Sajda, John Paisley
• Abstract summary: Several approximate inference methods have been proposed for deep discrete latent variable models. We propose a non-parametric iterative algorithm for learning discrete latent representations in such deep models. We evaluate our method across datasets with varying characteristics and compare our results to current amortized approximate inference methods.
• Score: 11.937283219047984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Several 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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