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Iterative Refinement of the Approximate Posterior for Directed Belief Networks

NeurIPS 2016 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Amortized and online variational inference · 3 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Refine posterior with AIR at test time (RWS+ vs RWS)RWS-trained SBN 200 · MNISTtest log-likelihood estimate (nats): 102.51 -> 102.00Helped
Refine posterior with AIR at test time (RWS+ vs RWS)RWS-trained SBN 200 · Caltech-101 Silhouettestest log-likelihood estimate (nats): 121.38 -> 118.63Helped
Refine posterior with AIR at test time (RWS+ vs RWS)RWS-trained SBN 200-200 · Caltech-101 Silhouettestest log-likelihood estimate (nats): 112.86 -> 107.20Helped
Refine posterior with AIR at test time (RWS+ vs RWS)RWS-trained SBN 200-200-200 · Caltech-101 Silhouettestest log-likelihood estimate (nats): 110.57 -> 104.54Helped
Train with AIR instead of RWSSBN 200 · MNISTtest log-likelihood estimate (nats): 102.51 -> 100.92Helped
Train with AIR instead of RWSSBN 200-200-200 · MNISTtest log-likelihood estimate (nats): 92.00 -> 92.56Hurt
Train with AIR instead of RWSDARN 500 · MNISTtest log-likelihood estimate (nats): 85.40 -> 85.46Hurt
Train with AIR instead of RWSSBN 200-200 · Caltech-101 Silhouettestest log-likelihood estimate (nats): 112.86 -> 106.94Helped
Use 20 instead of 5 AIR refinement steps in training (ablation)AIR · DBN training, ESS and log-likelihoodtest log-likelihood estimate (nats)Helped
Add reweighting and inclusive KL (AIR+RW, AIR+RW+IKL) to AIR with 20 steps (ablation)AIR(20) · final log-likelihoodtest log-likelihood estimate (nats)Hurt
Add reweighting to AIR with 5 steps (ablation)AIR(5) · final log-likelihoodtest log-likelihood estimate (nats)Helped
Refine with 100 AIR steps vs no refinement1-layer SBN 20 units · exact log-probability -127.474test log-likelihood estimate (nats): -127.51 -> -127.48Helped

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