Fetching the paper…
Reading the bibliography…
Miscalibration - a mismatch between a model's confidence and its correctness - of Deep Neural Networks (DNNs) makes their predictions hard to rely on.
Verification of forecasts expressed in terms of probability
Glenn W Brier · 1950
Earlier work this paper cites.
The Comparison and Evaluation of Forecasters
Morris H DeGroot and Stephen E Fienberg · 1983
Earlier work this paper cites.
NewsWeeder: Learning to Filter Netnews
Ken Lang · 1995
Earlier work this paper cites.
On the Lambert W Function
Robert M Corless, Gaston H Gonnet, David EG Hare, David J Jeffrey, and Donald E Knuth · 1996
Earlier work this paper cites.
Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods
John Platt · 1999
Earlier work this paper cites.
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
Bianca Zadrozny and Charles Elkan · 2001
Earlier work this paper cites.
Transforming Classifier Scores into Accurate Multiclass Probability Estimates
Bianca Zadrozny and Charles Elkan · 2002
Earlier work this paper cites.
Predicting Good Probabilities With Supervised Learning
Alexandru Niculescu-Mizil and Rich Caruana · 2005
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning Multiple Layers of Features from Tiny Images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Reading Digits in Natural Images with Unsupervised Feature Learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Ng, and Christopher Potts · 2013
Earlier work this paper cites.
Network In Network
Min Lin, Qiang Chen, and Shuicheng Yan · 2014
Earlier work this paper cites.
GloVe: Global Vectors for Word Representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
Earlier work this paper cites.
Obtaining Well Calibrated Probabilities Using Bayesian Binning
Mahdi Pakdaman Naeini, Gregory F Cooper, and Milos Hauskrecht · 2015
Cited alongside, same era.
Posterior calibration and exploratory analysis for natural language processing models
Khanh Nguyen and Brendan O’Connor · 2015
Cited alongside, same era.
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
Kai Sheng Tai, Richard Socher, and Christopher D Manning · 2015
Cited alongside, same era.
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Binary Classifier Calibration using an Ensemble of Near Isotonic Regression Models
Mahdi Pakdaman Naeini and Gregory F Cooper · 2016
Cited alongside, same era.
Wide Residual Networks
Deep Learning Applications in Medical Image Analysis
Justin Ker, Lipo Wang, Jai Rao, and Tchoyoson Lim · 2018
Later among the works it cites.
Trainable Calibration Measures For Neural Networks From Kernel Mean Embeddings
Aviral Kumar, Sunita Sarawagi, and Ujjwal Jain · 2018
Later among the works it cites.
Deep learning for smart manufacturing: Methods and applications
Jinjiang Wang, Yulin Ma, Laibin Zhang, Robert X Gao, and Dazhong Wu · 2018
Later among the works it cites.
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Dan Hendrycks and Thomas Dietterich · 2019
Later among the works it cites.
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration
Meelis Kull, Miquel Perello Nieto, Markus Kängsepp, Telmo Silva Filho, Hao Song, and Peter Flach · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
On Calibration of Modern Neural Networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Densely Connected Convolutional Networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Focal Loss for Dense Object Detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
Cited alongside, same era.
Regularizing Neural Networks by Penalizing Confident Output Distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Łukasz Kaiser, and Geoffrey Hinton · 2017
Cited alongside, same era.
Attention Is All You Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Deep Learning and Its Applications in Biomedicine
Chensi Cao, Feng Liu, Hai Tan, Deshou Song, Wenjie Shu, Weizhong Li, Yiming Zhou, Xiaochen Bo, and Zhi Xie · 2018
Cited alongside, same era.
Verified Uncertainty Calibration
Ananya Kumar, Percy S Liang, and Tengyu Ma · 2019
Later among the works it cites.
When Does Label Smoothing Help?
Rafael Müller, Simon Kornblith, and Geoffrey Hinton · 2019
Later among the works it cites.
20 Newsgroups
Ng · 2019
Later among the works it cites.
Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D Sculley, Sebastian Nowozin, Joshua V Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
Later among the works it cites.
Train CIFAR10 with PyTorch, 2019
PyTorch-CIFAR · 2019
Later among the works it cites.
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks
Sunil Thulasidasan, Gopinath Chennupati, Jeff A Bilmes, Tanmoy Bhattacharya, and Sarah Michalak · 2019
Later among the works it cites.
TreeLSTM
TreeLSTM · 2019
Later among the works it cites.
Evaluating model calibration in classification
Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, and Thomas B Schön · 2019
Later among the works it cites.
Calibration tests in multi-class classification: A unifying framework
David Widmann, Fredrik Lindsten, and Dave Zachariah · 2019
Later among the works it cites.