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Variational Russian Roulette for Variational Russian Roulette for Deep Bayesian Nonparametrics Deep Bayesian Nonparametrics Kai Xu [1] Joint work with Akash Srivastava [1,2] and Charles Sutton [1,3,4] [1] University of Edinburgh [2] MIT-IBM


  1. Variational Russian Roulette for Variational Russian Roulette for Deep Bayesian Nonparametrics Deep Bayesian Nonparametrics Kai Xu [1] Joint work with Akash Srivastava [1,2] and Charles Sutton [1,3,4] [1] University of Edinburgh [2] MIT-IBM Watson AI Lab [3] Google AI [4] Alan Turing Institute 1

  2. tl;dr tl;dr We train a variational autoencoder with unbounded latent dimension . The latent dimension is controlled by a sparse binary matrix with infinitely many columns , following an Indian buffet process . The actual dimensionality of the VAE is inferred during training . 2

  3. How an infinite binary matrix is useful for a VAE? How an infinite binary matrix is useful for a VAE? m is a sparse binary matrix with finite number of rows (same as number of data) and infinitely many columns Fig. 1 Infinite VAE with an IBP prior (Chatzis, 2014; Singh et al. 2017) 3

  4. Truncation-free variational approximation Truncation-free variational approximation Previous work uses truncated variational approximation. Our method avoids using truncated approximation. Why a truncated variational approximation is not ideal? 1. Truncation level is not easy to choose. 2. Poor interaction with amortised inference. How do we avoid truncating variational posterior at all? 4

  5. RAVE: Roulette-based Amortized Variational Expectations RAVE: Roulette-based Amortized Variational Expectations 1. Introduce a new infinite variational approximation Essentially an infinite mixture of truncated approximations 2. Derive a new tractable ELBO Essentially an infinite mixture of truncated ELBOs 3. Compute an unbiased gradient estimate of the ELBO Infinite summation is estimated by Russian roulette sampling At any time, we only retain a finite representation in memory to compute the unbiased gradient estimate of the infinite target . 5

  6. Results Results Truncated approximation tends to activate collapsed component Only first few components are informative Dimensions convey no information Russian roulette (marked by red vertical lines) automatically truncates at right dimension Non-informative components are still activated Please come to check my poster @Pacific Ballroom #223 6

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