Who Cited It

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

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

On Calibration of Modern Neural Networks (2017)On Calibration of Modern Neur…Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh… (2024)Batch Normalization: Accelera…Distilling the Knowledge in a Neural Network (2015)Distilling the Knowledge in a…Deep Learning (2016)Deep LearningRecursive Deep Models for Semantic Compositionality Over a Sentiment Treebank (2013)Recursive Deep Models for Sem…Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likeliho… (1999)Probabilistic Outputs for Sup…Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep Learning (2015)Dropout as a Bayesian Approxi…A Practical Bayesian Framework for Backpropagation Networks (1992)A Practical Bayesian Framewor…Predicting good probabilities with supervised learning (2005)Predicting good probabilities…Intelligible Models for HealthCare (2015)Intelligible Models for Healt…A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks (2016)A Baseline for Detecting Misc…A review of uncertainty quantification in deep learning: Techniques, applications and cha… (2021)A review of uncertainty quant…
11 of 11 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
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What this paper cites, inside the corpus

PaperYearCited
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…202415,663
Distilling the Knowledge in a Neural Network201514,099
Deep Learning20168,952
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank20136,849
Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likeliho…19994,873
Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep Learning20154,187
A Practical Bayesian Framework for Backpropagation Networks19922,962
Predicting good probabilities with supervised learning20051,751
Intelligible Models for HealthCare20151,693
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks20161,627

What cites it, inside the corpus

PaperYearCited
A review of uncertainty quantification in deep learning: Techniques, applications and cha…20212,644

Links

DOI · Open access copy · OpenAlex record

Topics

Anomaly Detection Techniques and ApplicationsComputer Science
Adversarial Robustness in Machine LearningComputer Science
Machine Learning and Data ClassificationComputer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • 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.

sha256 db1645b78a57e29a…