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Deep ensembles fail to capture uncertainty in graph neural networks

By PulseAugur Editorial · [2 sources] ·

A new research paper questions the effectiveness of deep ensembles for uncertainty quantification in graph neural networks. The study found that ensembles offer minimal improvement over single models, with gains primarily from stabilizing predictions rather than improving uncertainty estimates. This is attributed to "epistemic collapse," where independently trained networks produce overly similar predictions, neutralizing the core advantage of ensembles. AI

IMPACT Challenges a common method for assessing model reliability in graph-based AI systems.

RANK_REASON Research paper published on arXiv investigating a specific machine learning technique.

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 English(EN) · Pedro C. Vieira, Pedro Ribeiro, Viacheslav Borovitskiy ·

    Do Deep Ensembles Actually Capture Uncertainty in Graph Neural Networks?

    arXiv:2605.22593v1 Announce Type: new Abstract: While deep ensembles are widely considered to be the default method for uncertainty quantification in deep learning, their effectiveness for graph-structured data is often simply assumed based on successes in domains like computer v…

  2. arXiv cs.LG TIER_1 English(EN) · Viacheslav Borovitskiy ·

    Do Deep Ensembles Actually Capture Uncertainty in Graph Neural Networks?

    While deep ensembles are widely considered to be the default method for uncertainty quantification in deep learning, their effectiveness for graph-structured data is often simply assumed based on successes in domains like computer vision. We investigate standard deep ensembles sp…

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