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New method jointly optimizes neural network accuracy and calibration

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel method for jointly optimizing neural network accuracy and calibration, eliminating the need for separate post-hoc calibration steps. This bilevel Stackelberg formulation integrates calibration directly into the training process, using cross-entropy to simultaneously improve both metrics. The approach demonstrates that using the final classification layer as the calibrator is sufficient for achieving accurate and well-calibrated models with minimal added complexity, outperforming existing state-of-the-art calibration techniques. AI

IMPACT This research could lead to more reliable and trustworthy neural network outputs in various AI applications.

RANK_REASON The cluster contains a research paper detailing a new method for neural network calibration. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

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

New method jointly optimizes neural network accuracy and calibration

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The cluster contains a research paper detailing a new method for neural network calibration. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Nektarios Kalampalikis, Pablo M. Olmos, Isabel Valera ·

    Calibration in One Go: Jointly Learning Accurate and Calibrated Neural Networks

    arXiv:2610.13693v1 Announce Type: cross Abstract: Neural networks (NNs), while highly flexible and versatile, are often poorly calibrated. Post-hoc calibration methods adjust the outputs of an already trained model using a separate calibration dataset and additional calibration p…

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