On Calibration of Modern Neural Networks

Chuan Guo; Geoff Pleiss; Yu Sun; Kilian Q. Weinberger

doi:https://doi.org/10.48550/arxiv.1706.04599

On Calibration of Modern Neural Networks

2017 · arXiv (Cornell University) · 1,741 citations · 1 from inside this corpus

Chuan Guo, Geoff Pleiss low, Yu Sun, Kilian Q. Weinberger

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Citation edge evidence

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A review of uncertainty quantification in deep learning: Techniques, applications and cha…→On Calibration of Modern Neural NetworksLegacy edge — source detail unavailable
On Calibration of Modern Neural Networks→Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…Legacy edge — source detail unavailable
On Calibration of Modern Neural Networks→Distilling the Knowledge in a Neural NetworkLegacy edge — source detail unavailable
On Calibration of Modern Neural Networks→Deep LearningLegacy edge — source detail unavailable
On Calibration of Modern Neural Networks→Recursive Deep Models for Semantic Compositionality Over a Sentiment TreebankLegacy edge — source detail unavailable
On Calibration of Modern Neural Networks→Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likeliho…Legacy edge — source detail unavailable
On Calibration of Modern Neural Networks→Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep LearningLegacy edge — source detail unavailable
On Calibration of Modern Neural Networks→A Practical Bayesian Framework for Backpropagation NetworksLegacy edge — source detail unavailable
On Calibration of Modern Neural Networks→Predicting good probabilities with supervised learningLegacy edge — source detail unavailable
On Calibration of Modern Neural Networks→Intelligible Models for HealthCareLegacy edge — source detail unavailable
On Calibration of Modern Neural Networks→A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural NetworksLegacy edge — source detail unavailable
Predicting good probabilities with supervised learning→Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likeliho…Legacy edge — source detail unavailable
Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep Learning→A Practical Bayesian Framework for Backpropagation NetworksLegacy edge — source detail unavailable
A review of uncertainty quantification in deep learning: Techniques, applications and cha…→Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep LearningLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…	2024	15,663
Distilling the Knowledge in a Neural Network	2015	14,099
Deep Learning	2016	8,952
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank	2013	6,849
Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likeliho…	1999	4,873
Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep Learning	2015	4,187
A Practical Bayesian Framework for Backpropagation Networks	1992	2,962
Predicting good probabilities with supervised learning	2005	1,751
Intelligible Models for HealthCare	2015	1,693
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks	2016	1,627

What cites it, inside the corpus

Paper	Year	Cited
A review of uncertainty quantification in deep learning: Techniques, applications and cha…	2021	2,644

Links

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Topics

Anomaly Detection Techniques and Applications	Computer Science
Adversarial Robustness in Machine Learning	Computer Science
Machine Learning and Data Classification	Computer Science

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complete

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supports4 author record(s) attached.
supports37 reference(s) recorded.
neutralThe DOI carries no year to check against.
supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:53+00:00.

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