We propose Neural Calibration, a new calibration method, which learns to calibrate by making full use of all input information over the validation set. We test ...
May 26, 2019 · We propose Neural Calibration, a new calibration method, which learns to calibrate by making full use of all input information over the ...
Sep 5, 2023 · Towards reliable and fair probabilistic predictions: field-aware calibration with neural networks. Feiyang Pan, Xiang Ao, Pingzhong Tang, Min Lu ...
The results showed that Neural Calibration significantly improves against uncalibrated predictions in common metrics such as the negative log-likelihood, Brier ...
Apr 20, 2020 · The results showed that Neural Calibration significantly improves against uncalibrated predictions in common metrics such as the negative log- ...
May 26, 2019 · We propose Neural Calibration, a simple yet powerful post-hoc calibration method that learns to calibrate by making full use of the field-aware information ...
Missing: fair networks.
Here, we propose an efficient yet general modelling approach for obtaining well- calibrated, trustworthy probabilities for samples obtained af- ter a domain ...
Missing: reliable | Show results with:reliable
Mar 4, 2020 · To this end, we propose Neural Calibration, a simple yet powerful post-hoc calibration method that learns to calibrate by making full use of the ...
Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions · Computer Science. The Web Conference · 2020.
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In this paper, we put forward an evaluation metric for calibration, coined field-level calibration error, that measures bias in predictions over the input ...