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On Calibration of Modern Neural Networks
v1v2 (latest)

On Calibration of Modern Neural Networks

International Conference on Machine Learning (ICML), 2017
14 June 2017
Chuan Guo
Geoff Pleiss
Yu Sun
Kilian Q. Weinberger
    UQCV
ArXiv (abs)PDFHTML

Papers citing "On Calibration of Modern Neural Networks"

50 / 3,763 papers shown
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot
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Learning Adaptive Classifiers Synthesis for Generalized Few-Shot LearningInternational Journal of Computer Vision (IJCV), 2019
Han-Jia Ye
Hexiang Hu
De-Chuan Zhan
288
65
0
07 Jun 2019
Likelihood Ratios for Out-of-Distribution Detection
Likelihood Ratios for Out-of-Distribution DetectionNeural Information Processing Systems (NeurIPS), 2019
Jie Jessie Ren
Peter J. Liu
Emily Fertig
Jasper Snoek
Ryan Poplin
M. DePristo
Joshua V. Dillon
Balaji Lakshminarayanan
OODD
603
786
0
07 Jun 2019
When Does Label Smoothing Help?
When Does Label Smoothing Help?Neural Information Processing Systems (NeurIPS), 2019
Rafael Müller
Simon Kornblith
Geoffrey E. Hinton
UQCV
797
2,216
0
06 Jun 2019
Can You Trust Your Model's Uncertainty? Evaluating Predictive
  Uncertainty Under Dataset Shift
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset ShiftNeural Information Processing Systems (NeurIPS), 2019
Yaniv Ovadia
Emily Fertig
Jie Jessie Ren
Zachary Nado
D. Sculley
Sebastian Nowozin
Joshua V. Dillon
Balaji Lakshminarayanan
Jasper Snoek
UQCV
1.1K
1,929
0
06 Jun 2019
Practical Deep Learning with Bayesian Principles
Practical Deep Learning with Bayesian PrinciplesNeural Information Processing Systems (NeurIPS), 2019
Kazuki Osawa
S. Swaroop
Anirudh Jain
Runa Eschenhagen
Richard Turner
Rio Yokota
Mohammad Emtiyaz Khan
BDLUQCV
445
267
0
06 Jun 2019
CCMI : Classifier based Conditional Mutual Information Estimation
CCMI : Classifier based Conditional Mutual Information EstimationConference on Uncertainty in Artificial Intelligence (UAI), 2019
Sudipto Mukherjee
Himanshu Asnani
Sreeram Kannan
VLM
263
86
0
05 Jun 2019
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer
  Vision
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
Fredrik K. Gustafsson
Martin Danelljan
Thomas B. Schon
OODUQCVBDL
290
327
0
04 Jun 2019
Reliable training and estimation of variance networks
Reliable training and estimation of variance networksNeural Information Processing Systems (NeurIPS), 2019
N. Detlefsen
Martin Jørgensen
Søren Hauberg
UQCV
283
97
0
04 Jun 2019
Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections
Modeling Uncertainty by Learning a Hierarchy of Deep Neural ConnectionsNeural Information Processing Systems (NeurIPS), 2019
R. Y. Rohekar
Yaniv Gurwicz
Shami Nisimov
Gal Novik
BDLUQCV
265
13
0
30 May 2019
Enhancing Simple Models by Exploiting What They Already Know
Enhancing Simple Models by Exploiting What They Already Know
Amit Dhurandhar
Karthikeyan Shanmugam
Ronny Luss
191
2
0
30 May 2019
Evaluating and Calibrating Uncertainty Prediction in Regression Tasks
Evaluating and Calibrating Uncertainty Prediction in Regression TasksItalian National Conference on Sensors (INS), 2019
Dan Levi
Liran Gispan
Niv Giladi
Ethan Fetaya
UQCV
396
173
0
28 May 2019
On Mixup Training: Improved Calibration and Predictive Uncertainty for
  Deep Neural Networks
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural NetworksNeural Information Processing Systems (NeurIPS), 2019
S. Thulasidasan
Gopinath Chennupati
J. Bilmes
Tanmoy Bhattacharya
S. Michalak
UQCV
541
588
0
27 May 2019
Field-aware Calibration: A Simple and Empirically Strong Method for
  Reliable Probabilistic Predictions
Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic PredictionsThe Web Conference (WWW), 2019
Feiyang Pan
Xiang Ao
Pingzhong Tang
Min Lu
Dapeng Liu
Lei Xiao
Qing He
283
27
0
26 May 2019
Hyperparameter-Free Out-of-Distribution Detection Using Softmax of
  Scaled Cosine Similarity
Hyperparameter-Free Out-of-Distribution Detection Using Softmax of Scaled Cosine Similarity
Engkarat Techapanurak
Masanori Suganuma
Takayuki Okatani
OODD
352
30
0
25 May 2019
Privacy Risks of Securing Machine Learning Models against Adversarial
  Examples
Privacy Risks of Securing Machine Learning Models against Adversarial ExamplesConference on Computer and Communications Security (CCS), 2019
Liwei Song
Reza Shokri
Prateek Mittal
SILMMIACVAAML
216
280
0
24 May 2019
Controlling Risk of Web Question Answering
Controlling Risk of Web Question AnsweringAnnual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2019
Lixin Su
Jiafeng Guo
Yixing Fan
Yanyan Lan
Xueqi Cheng
178
9
0
24 May 2019
Multi-Class Gaussian Process Classification Made Conjugate: Efficient
  Inference via Data Augmentation
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data AugmentationConference on Uncertainty in Artificial Intelligence (UAI), 2019
Théo Galy-Fajou
F. Wenzel
Christian Donner
Manfred Opper
159
31
0
23 May 2019
Implicit Background Estimation for Semantic Segmentation
Implicit Background Estimation for Semantic SegmentationInternational Conference on Information Photonics (ICIP), 2019
Charles Lehman
Dogancan Temel
G. Al-Regib
VLM
146
2
0
23 May 2019
Ensemble Model Patching: A Parameter-Efficient Variational Bayesian
  Neural Network
Ensemble Model Patching: A Parameter-Efficient Variational Bayesian Neural Network
Oscar Chang
Yuling Yao
David Williams-King
Hod Lipson
BDLUQCV
195
8
0
23 May 2019
Detecting Adversarial Examples and Other Misclassifications in Neural
  Networks by Introspection
Detecting Adversarial Examples and Other Misclassifications in Neural Networks by Introspection
Jonathan Aigrain
Marcin Detyniecki
AAML
158
30
0
22 May 2019
Survival Function Matching for Calibrated Time-to-Event Predictions
Survival Function Matching for Calibrated Time-to-Event Predictions
Paidamoyo Chapfuwa
Chenyang Tao
Lawrence Carin
Ricardo Henao
OOD
98
4
0
21 May 2019
Distribution Calibration for Regression
Distribution Calibration for RegressionInternational Conference on Machine Learning (ICML), 2019
Hao Song
Tom Diethe
Meelis Kull
Peter A. Flach
UQCV
335
122
0
15 May 2019
What Clinicians Want: Contextualizing Explainable Machine Learning for
  Clinical End Use
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End UseMachine Learning in Health Care (MLHC), 2019
S. Tonekaboni
Shalmali Joshi
M. Mccradden
Anna Goldenberg
312
503
0
13 May 2019
Learning Representations for Predicting Future Activities
Learning Representations for Predicting Future Activities
Mohammadreza Zolfaghari
Özgün Çiçek
S. M. Ali
F. Mahdisoltani
Can Zhang
Thomas Brox
AI4TS
201
6
0
09 May 2019
Are Graph Neural Networks Miscalibrated?
Are Graph Neural Networks Miscalibrated?
Leonardo Teixeira
B. Jalaeian
Bruno Ribeiro
AI4CE
192
24
0
07 May 2019
Unsupervised Temperature Scaling: An Unsupervised Post-Processing
  Calibration Method of Deep Networks
Unsupervised Temperature Scaling: An Unsupervised Post-Processing Calibration Method of Deep Networks
A. Mozafari
H. Gomes
Wilson Leão
Christian Gagné
UQCV
157
3
0
01 May 2019
Test Selection for Deep Learning Systems
Test Selection for Deep Learning SystemsACM Transactions on Software Engineering and Methodology (TOSEM), 2019
Wei Ma
Mike Papadakis
Anestis Tsakmalis
Maxime Cordy
Yves Le Traon
OOD
150
104
0
30 Apr 2019
Realizing Petabyte Scale Acoustic Modeling
Realizing Petabyte Scale Acoustic Modeling
S. Parthasarathi
Nitin Sivakrishnan
Pranav Ladkat
N. Strom
117
11
0
24 Apr 2019
Acoustic scene classification using teacher-student learning with
  soft-labels
Acoustic scene classification using teacher-student learning with soft-labels
Hee-Soo Heo
Jee-weon Jung
Hye-jin Shim
Ha-Jin Yu
181
24
0
23 Apr 2019
Convolutional Neural Networks for Classification of Alzheimer's Disease:
  Overview and Reproducible Evaluation
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation
Junhao Wen
Elina Thibeau-Sutre
Mauricio Diaz-Melo
J. Samper-González
A. Routier
Simona Bottani
Didier Dormont
S. Durrleman
Ninon Burgos
O. Colliot
384
621
0
16 Apr 2019
Quizbowl: The Case for Incremental Question Answering
Quizbowl: The Case for Incremental Question Answering
Pedro Rodriguez
Shi Feng
Mohit Iyyer
He He
Jordan L. Boyd-Graber
274
54
0
09 Apr 2019
The Fishyscapes Benchmark: Measuring Blind Spots in Semantic
  Segmentation
The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation
Hermann Blum
Paul-Edouard Sarlin
Juan I. Nieto
Roland Siegwart
Cesar Cadena
UQCV
395
180
0
05 Apr 2019
Automatic detection of lesion load change in Multiple Sclerosis using
  convolutional neural networks with segmentation confidence
Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence
Richard McKinley
L. Grunder
Rik Wepfer
F. Aschwanden
Tim Fischer
...
M. Reyes
A. Salmen
A. Chan
Roland Wiest
F. Wagner
124
65
0
05 Apr 2019
Minimum Uncertainty Based Detection of Adversaries in Deep Neural
  Networks
Minimum Uncertainty Based Detection of Adversaries in Deep Neural Networks
Fatemeh Sheikholeslami
Swayambhoo Jain
G. Giannakis
AAML
188
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0
05 Apr 2019
Measuring Calibration in Deep Learning
Measuring Calibration in Deep Learning
Jeremy Nixon
Michael W. Dusenberry
Ghassen Jerfel
Timothy Nguyen
Jeremiah Zhe Liu
Linchuan Zhang
Dustin Tran
UQCV
560
581
0
02 Apr 2019
Lessons from Building Acoustic Models with a Million Hours of Speech
Lessons from Building Acoustic Models with a Million Hours of Speech
S. Parthasarathi
N. Strom
200
89
0
02 Apr 2019
Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild
Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild
Kibok Lee
Kimin Lee
Jinwoo Shin
Honglak Lee
CLL
413
227
0
29 Mar 2019
Benchmarking Neural Network Robustness to Common Corruptions and
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Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Dan Hendrycks
Thomas G. Dietterich
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28 Mar 2019
The Algorithmic Automation Problem: Prediction, Triage, and Human Effort
The Algorithmic Automation Problem: Prediction, Triage, and Human Effort
M. Raghu
Katy Blumer
G. Corrado
Jon M. Kleinberg
Ziad Obermeyer
S. Mullainathan
336
154
0
28 Mar 2019
Combination of Multiple Global Descriptors for Image Retrieval
Combination of Multiple Global Descriptors for Image Retrieval
HeeJae Jun
ByungSoo Ko
Youngjoon Kim
Insik Kim
Jongtack Kim
343
65
0
26 Mar 2019
Interpreting Neural Networks Using Flip Points
Interpreting Neural Networks Using Flip Points
Roozbeh Yousefzadeh
D. O’Leary
AAMLFAtt
146
18
0
21 Mar 2019
Performance Measurement for Deep Bayesian Neural Network
Yikuan Li
Yajie Zhu
BDLOODUQCV
107
1
0
20 Mar 2019
On Class Imbalance and Background Filtering in Visual Relationship
  Detection
On Class Imbalance and Background Filtering in Visual Relationship DetectionIEEE International Joint Conference on Neural Network (IJCNN), 2019
Alessio Sarullo
Tingting Mu
245
4
0
20 Mar 2019
Revisiting the Evaluation of Uncertainty Estimation and Its Application
  to Explore Model Complexity-Uncertainty Trade-Off
Revisiting the Evaluation of Uncertainty Estimation and Its Application to Explore Model Complexity-Uncertainty Trade-Off
Yukun Ding
Jinglan Liu
Jinjun Xiong
Yiyu Shi
149
14
0
05 Mar 2019
Towards Structured Evaluation of Deep Neural Network Supervisors
Towards Structured Evaluation of Deep Neural Network SupervisorsInternational Conference on Artificial Intelligence Testing (ICAIT), 2019
Jens Henriksson
C. Berger
Markus Borg
Lars Tornberg
Cristofer Englund
S. Sathyamoorthy
Stig Ursing
AAML
178
39
0
04 Mar 2019
Calibration of Encoder Decoder Models for Neural Machine Translation
Calibration of Encoder Decoder Models for Neural Machine Translation
Aviral Kumar
Sunita Sarawagi
367
108
0
03 Mar 2019
Deep learning in bioinformatics: introduction, application, and
  perspective in big data era
Deep learning in bioinformatics: introduction, application, and perspective in big data erabioRxiv (bioRxiv), 2019
Yu Li
Chao Huang
Lizhong Ding
Zhongxiao Li
Yijie Pan
Xin Gao
AI4CE
250
322
0
28 Feb 2019
Evaluating model calibration in classification
Evaluating model calibration in classification
Juozas Vaicenavicius
David Widmann
Carl R. Andersson
Fredrik Lindsten
Jacob Roll
Thomas B. Schon
UQCV
364
234
0
19 Feb 2019
ProtoAttend: Attention-Based Prototypical Learning
ProtoAttend: Attention-Based Prototypical Learning
Sercan O. Arik
Tomas Pfister
327
20
0
17 Feb 2019
Neural Inverse Knitting: From Images to Manufacturing Instructions
Neural Inverse Knitting: From Images to Manufacturing Instructions
Alexandre Kaspar
Tae-Hyun Oh
L. Makatura
Petr Kellnhofer
Jacqueline Aslarus
Wojciech Matusik
173
34
0
07 Feb 2019
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