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New OM-Path method improves Deep Gaussian Process inference

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced OM-Path, a novel method for approximate inference in Deep Gaussian Processes (DGPs). This approach frames inference as posterior transport, learning a deterministic sampler to map a reference measure to inducing variables. OM-Path utilizes the Onsager-Machlup action as a path regularizer and has shown statistically significant improvements over existing methods like DBVI on specific large datasets, though it performs comparably on smaller, noisier datasets. AI

IMPACT Introduces a novel inference technique that may enhance the performance of Deep Gaussian Processes on complex regression tasks.

RANK_REASON The cluster contains an academic paper detailing a new method for machine learning.

Read on arXiv cs.LG →

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

COVERAGE [2]

  1. arXiv cs.LG TIER_1 · Jian Xu, Delu Zeng, John Paisley, Qibin Zhao ·

    Onsager-Machlup Posterior Transport for Deep Gaussian Processes

    arXiv:2605.23434v1 Announce Type: new Abstract: Approximate inference over inducing variables is the central computational bottleneck of Deep Gaussian Processes (DGPs). Existing methods either fit an explicit density $q_\phi(\bU)$ by an ELBO (DSVI, IPVI, DDVI, DBVI) or sample by …

  2. arXiv cs.LG TIER_1 · Qibin Zhao ·

    Onsager-Machlup Posterior Transport for Deep Gaussian Processes

    Approximate inference over inducing variables is the central computational bottleneck of Deep Gaussian Processes (DGPs). Existing methods either fit an explicit density $q_φ(\bU)$ by an ELBO (DSVI, IPVI, DDVI, DBVI) or sample by MCMC (SGHMC). We instead frame DGP inference as \em…

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