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New paper explores faster uncertainty quantification for deep neural networks

By PulseAugur Editorial · [2 sources] ·

Researchers have published a paper on arXiv detailing Score-Based Martingale Posteriors (SMPs) for deep neural networks. This method offers a potentially faster alternative to traditional Markov chain Monte Carlo techniques for uncertainty quantification in machine learning. The paper explores SMPs for inferring parameters in deep neural networks and compares their efficacy to existing Monte Carlo methods. AI

IMPACT This research could lead to more efficient uncertainty quantification in deep learning models.

RANK_REASON The cluster contains an academic paper published on arXiv detailing a new statistical method for deep neural networks.

Read on arXiv stat.ML →

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

New paper explores faster uncertainty quantification for deep neural networks

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The cluster contains an academic paper published on arXiv detailing a new statistical method for deep neural networks.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Abylay Zhumekenov, Ajay Jasra, Mohamed Maama, Raul Tempone ·

    Score-Based Martingale Posteriors for Deep Neural Networks

    arXiv:2606.15725v1 Announce Type: cross Abstract: In this paper we investigate the efficacy of the score-based martingale posteriors (SMP) (Cui & Walker, 2025; Fong et al., 2023) in the context of modern and large-scale machine learning problems and its potential for meaningful u…

  2. arXiv stat.ML TIER_1 English(EN) · Raul Tempone ·

    Score-Based Martingale Posteriors for Deep Neural Networks

    In this paper we investigate the efficacy of the score-based martingale posteriors (SMP) (Cui & Walker, 2025; Fong et al., 2023) in the context of modern and large-scale machine learning problems and its potential for meaningful uncertainty quantification. SMPs work with a stocha…

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