Andrew Geng, Pin-Yu Chen
IEEE SaTML 2024
With the advancement of deep learning technology, neural networks have demonstrated their excellent ability to provide accurate predictions in many tasks. However, a lack of consideration for neural network calibration will not gain trust from humans, even for high-accuracy models. In this regard, the gap between the confidence of the model's predictions and the actual correctness likelihood must be bridged to derive a well-calibrated model. In this paper, we introduce the Neural Clamping Toolkit, the first open-source framework designed to help developers employ state-of-the-art model-agnostic calibrated models. Furthermore, we provide animations and interactive sections in the demonstration to familiarize researchers with calibration in neural networks. A Colab tutorial on utilizing our toolkit is also introduced.
Andrew Geng, Pin-Yu Chen
IEEE SaTML 2024
Zhiyuan He, Yijun Yang, et al.
ICML 2024
Gaoyuan Zhang, Songtao Lu, et al.
UAI 2022
Heshan Fernando, Lisha Chen, et al.
ICASSP 2024