New methods improve uncertainty quantification in deep neural networks

A new paper introduces two novel methods, Gradient-Laplace and Greedy-Laplace, for approximating Hessian matrices in deep neural networks. These methods aim to improve uncertainty quantification by addressing the computational challenges of the full Laplace approximation. The research demonstrates that existing sub-network Laplace approximations tend to underestimate predictive variance, and the proposed methods offer theoretical guarantees for optimality and improved performance over existing heuristic approaches. AI

IMPACT These methods could lead to more reliable uncertainty quantification in deep learning models, improving their trustworthiness in critical applications.

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

Read on arXiv stat.ML →

• Gradient-Laplace
• Greedy-Laplace
• Hessian matrices
• Laplace Approximation
• sub-network Laplace approximations
• Swarnali Raha

• paper
• model release

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