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State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes

ICML 2020 · Probabilistic and scientific modeling › Uncertainty and Gaussian processes · approach: Sparse variational Gaussian processes · 4 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use state-space EEP instead of batch EPGP inference · CoalNLPD: 0.922±0.11 -> 0.922±0.11No clear effect
use state-space EEP instead of batch EPBanana · BananaNLPD: 0.228±0.07 vs 0.216±0.10No clear effect
use EEP linearisation rather than moment matchingGP inference · MotorcycleNLPD: 0.855±0.25 vs 0.444±0.32Hurt
use EEP linearisation rather than EP moment matchingGP inference · AudioNLPD: -0.433±0.04 vs -0.321±0.15Mixed
add full site covariance terms through EEPAudio GP model · AudioNLPD: -0.433±0.04Helped
set EP power α=0 instead of α=1 (ablation)EEP · AudioNLPD: -0.433±0.04 -> -0.570±0.04Helped
set EP power α=0 instead of α=1 (ablation)EEP · RainforestNLPD: 0.325±0.01 -> 0.309±0.01Helped
set EP power α=0 instead of α=1 (ablation)EEP · CoalNLPD: 0.922±0.11 -> 0.922±0.11No clear effect
set EP power α=0 instead of α=1 (ablation)EEP · BananaNLPD: 0.228±0.07 -> 0.229±0.07No clear effect
use EEP rather than UEPAudio GP model · Audioruntime: 1.661 -> 1.176Helped
use state-space inference instead of batch methodslarge temporal GP models · RainforestapplicabilityHelped

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