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Research map / Probabilistic and scientific modeling / Variational and generative sampling

Variational Bayes on Monte Carlo Steroids

NeurIPS 2016 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Variational inference theory and gradients

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add random parity-constraint projections (VB-MCS, k=5, median aggregation T=3)sigmoid belief network with neural inference network · Caltech 101 SilhouettesNLL (lower better): 251.04 -> 245.60Helped
add random parity-constraint projections (VB-MCS, k=5/10/20)sigmoid belief network with neural inference network · NIPS Proceedingslog-perplexity (lower better): 5009.79 -> 4919.35 (k=5), 4919.22 (k=10), 4920.71 (k=20)Helped
increase number of parity constraints k from 5 to 20 (ablation)k-SBN with VB-MCS · Caltech 101 SilhouettesNLL (lower better): 245.60 (k=5) -> 248.79 (k=10) -> 256.60 (k=20)Hurt
vary number of parity constraints k across 5, 10, 20 (ablation)k-SBN with VB-MCS · NIPS Proceedingslog-perplexity (lower better): 4919.35 / 4919.22 / 4920.71No clear effect

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