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Research paper details three costs of amortizing Gaussian Process inference

By PulseAugur Editorial · [2 sources] ·

A new research paper details three primary costs associated with amortizing Gaussian Process inference using Neural Processes. The study identifies label contamination, an information bottleneck, and amortization error as key factors. The paper provides mathematical bounds for these costs and offers architectural recommendations to improve efficiency and accuracy in this domain. AI

IMPACT Characterizes the trade-offs in amortizing Gaussian Process inference, offering insights for researchers developing more efficient probabilistic models.

RANK_REASON The cluster contains an academic paper detailing a novel research finding in machine learning.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Robin Young ·

    Three Costs of Amortizing Gaussian Process Inference with Neural Processes

    arXiv:2605.21798v1 Announce Type: cross Abstract: Neural processes amortize Gaussian process inference, replacing the exact $O(n^3)$ posterior with a learned $O(n)$ map from context sets to predictive distributions. For a class of latent neural processes, we bound the Kullback--L…

  2. arXiv stat.ML TIER_1 English(EN) · Robin Young ·

    Three Costs of Amortizing Gaussian Process Inference with Neural Processes

    Neural processes amortize Gaussian process inference, replacing the exact $O(n^3)$ posterior with a learned $O(n)$ map from context sets to predictive distributions. For a class of latent neural processes, we bound the Kullback--Leibler (KL) divergence between the GP and LNP pred…

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