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Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes

Bootstrapping neural processes

NeurIPS 2020 · Probabilistic and scientific modeling › Uncertainty and Gaussian processes · approach: Bayesian decision-making under uncertainty · 7 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add bootstrap resampling and bagging to the base neural processCNP · 1D regression Periodictarget log-likelihood: -1.747 -> -1.082Helped
Add bootstrap resampling and bagging to the attentive neural processCANP · EMNIST unseen classes (10-46)target log-likelihood: 0.834 -> 0.942Helped
Add bootstrap resampling and bagging to the attentive neural processCANP · Predator-prey realtarget log-likelihood: -8.007 -> -5.465Helped
Remove paired-bootstrap context resampling (ablation)BNP · 1D regression Periodictarget log-likelihood: -1.089 -> -1.342Hurt
Remove the adaptation layer (ablation)BNP · 1D regression RBFtarget log-likelihood: 0.523 -> 0.455Hurt
Remove the adaptation layer (ablation)BNP · 1D regression Periodictarget log-likelihood: -1.089 -> -0.845Helped
Remove the additional base-model loss (ablation)BNP · 1D regression RBFtarget log-likelihood: 0.523 -> 0.496Hurt

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