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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
A Simple Baseline for Bayesian Uncertainty in Deep Learning
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Dmitry Vetrov
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753
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Predictive Uncertainty Quantification with Compound Density Networks
Predictive Uncertainty Quantification with Compound Density Networks
Agustinus Kristiadi
Sina Daubener
Asja Fischer
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258
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04 Feb 2019
Collaborative Sampling in Generative Adversarial Networks
Collaborative Sampling in Generative Adversarial Networks
Yuejiang Liu
Parth Kothari
Alexandre Alahi
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311
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0
02 Feb 2019
Deep Active Learning for Efficient Training of a LiDAR 3D Object
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Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector
Di Feng
Xiao Wei
Lars Rosenbaum
A. Maki
Klaus C. J. Dietmayer
3DPC
186
94
0
29 Jan 2019
Using Pre-Training Can Improve Model Robustness and Uncertainty
Using Pre-Training Can Improve Model Robustness and Uncertainty
Dan Hendrycks
Kimin Lee
Mantas Mazeika
NoLa
560
792
0
28 Jan 2019
Testing Conditional Independence in Supervised Learning Algorithms
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Marvin N. Wright
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454
69
0
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Money on the Table: Statistical information ignored by Softmax can
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Money on the Table: Statistical information ignored by Softmax can improve classifier accuracy
Charles B. Delahunt
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J. Nathan Kutz
167
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0
26 Jan 2019
Improving Adversarial Robustness via Promoting Ensemble Diversity
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Tianyu Pang
Kun Xu
Chao Du
Ning Chen
Jun Zhu
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295
483
0
25 Jan 2019
Delta-training: Simple Semi-Supervised Text Classification using
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Delta-training: Simple Semi-Supervised Text Classification using Pretrained Word Embeddings
Hwiyeol Jo
Ceyda Cinarel
278
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Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at
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Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation
Amr M. Alexandari
A. Kundaje
Avanti Shrikumar
226
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21 Jan 2019
Physics-Constrained Deep Learning for High-dimensional Surrogate
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Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data
Yinhao Zhu
N. Zabaras
P. Koutsourelakis
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PINNAI4CE
350
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Certainty Driven Consistency Loss on Multi-Teacher Networks for
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Certainty Driven Consistency Loss on Multi-Teacher Networks for Semi-Supervised Learning
Lu Liu
R. Tan
413
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17 Jan 2019
Toward Explainable Fashion Recommendation
Toward Explainable Fashion Recommendation
Pongsate Tangseng
Takayuki Okatani
167
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A witness function based construction of discriminative models using
  Hermite polynomials
A witness function based construction of discriminative models using Hermite polynomials
H. Mhaskar
A. Cloninger
Xiuyuan Cheng
257
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0
10 Jan 2019
Can You Trust This Prediction? Auditing Pointwise Reliability After
  Learning
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning
Peter F. Schulam
Suchi Saria
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362
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0
02 Jan 2019
Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data
  Streams
Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams
Mohammad Kachuee
Orpaz Goldstein
Kimmo Karkkainen
Sajad Darabi
Majid Sarrafzadeh
OOD
248
37
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Why ReLU networks yield high-confidence predictions far away from the
  training data and how to mitigate the problem
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem
Matthias Hein
Maksym Andriushchenko
Julian Bitterwolf
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410
604
0
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Deep Anomaly Detection with Outlier Exposure
Deep Anomaly Detection with Outlier Exposure
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Knowing what you know in brain segmentation using Bayesian deep neural
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Knowing what you know in brain segmentation using Bayesian deep neural networks
Patrick McClure
Nao Rho
J. Lee
Jakub R. Kaczmarzyk
C. Zheng
Satrajit S. Ghosh
D. Nielson
Adam G. Thomas
P. Bandettini
Francisco Pereira
UQCV3DVBDL
217
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0
03 Dec 2018
XNet: A convolutional neural network (CNN) implementation for medical
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XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasets
Joseph Aylett-Bullock
C. Cuesta-Lázaro
Arnau Quera-Bofarull
221
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03 Dec 2018
Snapshot Distillation: Teacher-Student Optimization in One Generation
Snapshot Distillation: Teacher-Student Optimization in One Generation
Chenglin Yang
Lingxi Xie
Chi Su
Alan Yuille
221
220
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Online Abstraction with MDP Homomorphisms for Deep Learning
Online Abstraction with MDP Homomorphisms for Deep Learning
Ondrej Biza
Robert Platt
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157
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Evaluating Bayesian Deep Learning Methods for Semantic Segmentation
Evaluating Bayesian Deep Learning Methods for Semantic Segmentation
Jishnu Mukhoti
Y. Gal
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290
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Deep learning for pedestrians: backpropagation in CNNs
Deep learning for pedestrians: backpropagation in CNNs
L. Boué
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Calibrating Uncertainties in Object Localization Task
Calibrating Uncertainties in Object Localization Task
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152
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Label-Noise Robust Generative Adversarial Networks
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Tatsuya Harada
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249
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Probabilistic Object Detection: Definition and Evaluation
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Feras Dayoub
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Haoyang Zhang
Dimity Miller
Peter Corke
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A. Angelova
Niko Sünderhauf
UQCV
287
117
0
27 Nov 2018
Bayesian QuickNAT: Model Uncertainty in Deep Whole-Brain Segmentation
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Bayesian QuickNAT: Model Uncertainty in Deep Whole-Brain Segmentation for Structure-wise Quality ControlNeuroImage (NeuroImage), 2018
Abhijit Guha Roy
Sailesh Conjeti
Nassir Navab
Christian Wachinger
UQCV
133
131
0
24 Nov 2018
Seeing in the dark with recurrent convolutional neural networks
Seeing in the dark with recurrent convolutional neural networks
Till S. Hartmann
74
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Scalable agent alignment via reward modeling: a research direction
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Jan Leike
David M. Krueger
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Miljan Martic
Vishal Maini
Shane Legg
378
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Learning to Compensate Photovoltaic Power Fluctuations from Images of
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Felix Berkenkamp
J. Poland
Andreas Krause
67
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Co-Representation Learning For Classification and Novel Class Detection via Deep Networks
Zhuoyi Wang
Zelun Kong
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Swarup Chandra
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92
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Large-Scale Visual Active Learning with Deep Probabilistic Ensembles
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J. Álvarez
Adam Lesnikowski
BDLUQCV
291
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Active Learning using Deep Bayesian Networks for Surgical Workflow
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Active Learning using Deep Bayesian Networks for Surgical Workflow AnalysisInternational Journal of Computer Assisted Radiology and Surgery (IJCARS), 2018
S. Bodenstedt
Dominik Rivoir
L. Mündermann
M. Wagner
Michael Breucha
Beat Müller-Stich
S. T. Mees
Jürgen Weitz
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179
49
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08 Nov 2018
Deep Weighted Averaging Classifiers
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Michael J.Q. Zhang
Hao Tang
241
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Single-Model Uncertainties for Deep Learning
Single-Model Uncertainties for Deep Learning
Natasa Tagasovska
David Lopez-Paz
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Understanding Deep Neural Networks through Input Uncertainties
Understanding Deep Neural Networks through Input Uncertainties
Jayaraman J. Thiagarajan
Irene Kim
Rushil Anirudh
P. Bremer
UQCVAAML
198
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0
31 Oct 2018
Attended Temperature Scaling: A Practical Approach for Calibrating Deep
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Attended Temperature Scaling: A Practical Approach for Calibrating Deep Neural Networks
A. Mozafari
H. Gomes
Wilson Leão
Steeven Janny
Christian Gagné
246
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Scalable Gaussian Processes on Discrete Domains
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Good Initializations of Variational Bayes for Deep Models
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Shallow-Deep Networks: Understanding and Mitigating Network Overthinking
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Taming the Cross Entropy Loss
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Rainer Stiefelhagen
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Complementary-Label Learning for Arbitrary Losses and Models
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Rate Distortion For Model Compression: From Theory To Practice
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Sewoong Oh
188
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210
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Inhibited Softmax for Uncertainty Estimation in Neural Networks
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Mateusz Susik
Rafal Karczewski
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Learning for Single-Shot Confidence Calibration in Deep Neural Networks
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Bohyung Han
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452
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Dropout Distillation for Efficiently Estimating Model Confidence
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Ingmar Posner
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Deep Confidence: A Computationally Efficient Framework for Calculating
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I. Cortés-Ciriano
A. Bender
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179
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0
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