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Title: On Calibration of Modern Neural Networks
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 methods on state-of-the-art architectures with image and document classification datasets. Our analysis and experiments not only offer insights into neural network learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling -- a single-parameter variant of Platt Scaling -- is surprisingly effective at calibrating predictions.  more » « less
Award ID(s):
1525919
NSF-PAR ID:
10065067
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
ICML 2017
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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