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Deep Gaussian Processes show non-Gaussian limits below critical threshold

By PulseAugur Editorial · [2 sources] ·

Researchers have identified a critical threshold in compositional Gaussian Processes (GPs) that determines whether their behavior in deep models becomes degenerate or non-trivial. The study establishes a sharp bandwidth threshold, $r_c(d) = \Theta(\sqrt{d})$, above which the GP prior converges to constant functions. Below this threshold, the prior converges to non-Gaussian, non-degenerate distributions, offering a more useful probabilistic model for deep Bayesian networks. AI

IMPACT Identifies a critical threshold for deep Gaussian Processes, potentially enabling more robust Bayesian modeling in deep learning architectures.

RANK_REASON The cluster contains an academic paper detailing new theoretical findings 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) · Mark Kozdoba, Shie Mannor ·

    How Deep Are Deep GPs, Really? A Sharp Threshold and a Non-Gaussian Limit for Compositional GPs

    arXiv:2606.08218v1 Announce Type: cross Abstract: Compositional priors describe the generic properties of layered functions in deep Bayesian models, where deep neural networks with random weights are a canonical example.In the wide-network limit, the prior is a Gaussian process w…

  2. arXiv stat.ML TIER_1 English(EN) · Shie Mannor ·

    How Deep Are Deep GPs, Really? A Sharp Threshold and a Non-Gaussian Limit for Compositional GPs

    Compositional priors describe the generic properties of layered functions in deep Bayesian models, where deep neural networks with random weights are a canonical example.In the wide-network limit, the prior is a Gaussian process with a depth-dependent kernel, and its behaviour as…

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