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New methods improve Laplace approximation for neural network uncertainty

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new methods for approximating the Laplace approximation in deep neural networks, addressing the computational challenges of inverting large Hessian matrices. The proposed Gradient-Laplace and Greedy-Laplace methods offer principled ways to select parameters for sub-network approximations, aiming to reduce the underestimation of predictive variance inherent in existing heuristic approaches. Theoretical analysis and numerical studies suggest these new methods provide stronger optimality guarantees and outperform current benchmarks. AI

IMPACT Improves uncertainty quantification in deep learning models, potentially leading to more reliable AI systems.

RANK_REASON The cluster contains an academic paper detailing new methods and theoretical analysis for a machine learning technique. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

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New methods improve Laplace approximation for neural network uncertainty

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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Rohit K Patra ·

    Optimality of Sub-network Laplace Approximations: New Results and Methods

    Although the Laplace approximation offers a simple route to uncertainty quantification in deep neural networks, its reliance on inverting large Hessian matrices has motivated a range of computationally feasible low-dimensional or sparse approximations. A prominent class of such m…

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