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New framework simplifies neural network mixed-effects model implementation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework for implementing neural network mixed-effects models (NMMs) using Template Model Builder (TMB). This approach leverages automatic differentiation and Laplace approximation, allowing users to specify only the negative joint log-likelihood and regularization terms. The framework automatically integrates out random effects and computes exact gradients, removing the need for manual derivations or approximations. This method has been demonstrated to be efficient and flexible across various applications, with reproducible code available to encourage wider adoption. AI

IMPACT Simplifies the implementation of complex statistical models, potentially accelerating research in areas using neural networks.

RANK_REASON The cluster describes a new framework for implementing statistical models, presented in an academic paper. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework simplifies neural network mixed-effects model implementation

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The cluster describes a new framework for implementing statistical models, presented in an academic paper. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Nan Zheng, Hoi Yiu Cheung, Vibhu Sharma, James T. Thorson, Noel G. Cadigan ·

    Implementing neural network mixed-effects models in Template Model Builder (TMB)

    arXiv:2608.31133v1 Announce Type: cross Abstract: Neural network mixed-effects models (NMMs) have gained traction by combining the strong representation and predictive power of artificial neural networks with the capacity of mixed-effects modeling to capture complex correlation s…

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