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On Calibration of Modern Neural Networks
[article]
2017
arXiv
pre-print
Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration
arXiv:1706.04599v2
fatcat:ouwx5xxp5rc73o2nmsluedyi6e