Fetching the paper…
Reading the bibliography…
Recent works demonstrate that early layers in a neural network contain useful information for prediction.
Verification of forecasts expressed in terms of probability
Glenn W Brier et al · 1950
Earlier work this paper cites.
Stacked generalization
David H Wolpert · 1992
Earlier work this paper cites.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John Platt et al · 1999
Earlier work this paper cites.
Transforming classifier scores into accurate multiclass probability estimates
Bianca Zadrozny and Charles Elkan · 2002
Earlier work this paper cites.
Statistical comparisons of classifiers over multiple data sets
Janez Demšar · 2006
Earlier work this paper cites.
Some comparisons among quadratic, spherical, and logarithmic scoring rules
J Eric Bickel · 2007
Earlier work this paper cites.
Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E Raftery · 2007
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.
Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
Earlier work this paper cites.
Novel decompositions of proper scoring rules for classification: Score adjustment as precursor to calibration
Meelis Kull and Peter Flach · 2015
Earlier work this paper cites.
Deeply-supervised nets
Chen-Yu Lee, Saining Xie, Patrick Gallagher, Zhengyou Zhang, and Zhuowen Tu · 2015
Earlier work this paper cites.
Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory F. Cooper, and Milos Hauskrecht · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Earlier work this paper cites.
Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2016
Earlier work this paper cites.
Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Earlier work this paper cites.
Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2016
Earlier work this paper cites.
Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
Cited alongside, same era.
Branchynet: Fast inference via early exiting from deep neural networks
Surat Teerapittayanon, Bradley McDanel, and Hsiang-Tsung Kung · 2016
Cited alongside, same era.
Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests
Andrew J Vickers, Ben Van Calster, and Ewout W Steyerberg · 2016
Cited alongside, same era.
Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
A closer look at memorization in deep networks
Devansh Arpit, Stanisław Jastrzkebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, and Simon Lacoste-Julien · 2017
Cited alongside, same era.
A simple baseline for Bayesian uncertainty in deep learning
Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, and Andrew Gordon Wilson · 2019
Later among the works it cites.
Measuring calibration in deep learning
Jeremy Nixon, Mike Dusenberry, Ghassen Jerfel, Timothy Nguyen, Jeremiah Liu, Linchuan Zhang, and Dustin Tran · 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 Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
Later among the works it cites.
Transfusion: Understanding transfer learning for medical imaging
Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, and Samy Bengio · 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.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Adaptive neural networks for fast test-time prediction
Tolga Bolukbasi, Joseph Wang, Ofer Dekel, and Venkatesh Saligrama · 2017
Cited alongside, same era.
Algorithmic decision making and the cost of fairness
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq · 2017
Cited alongside, same era.
Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv · 2017
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.
Multi-scale dense networks for resource efficient image classification
Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Cited alongside, same era.
DNN or k-NN: That is the generalize vs. memorize question
Gilad Cohen, Guillermo Sapiro, and Raja Giryes · 2018
Cited alongside, same era.
Trainable calibration measures for neural networks from kernel mean embeddings
Aviral Kumar, Sunita Sarawagi, and Ujjwal Jain · 2018
Cited alongside, same era.
Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
Jeremiah Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax Weiss, and Balaji Lakshminarayanan · 2020
Later among the works it cites.
What do neural networks learn when trained with random labels?
Hartmut Maennel, Ibrahim Alabdulmohsin, Ilya Tolstikhin, Robert JN Baldock, Olivier Bousquet, Sylvain Gelly, and Daniel Keysers · 2020
Later among the works it cites.
Non-parametric calibration for classification
Jonathan Wenger, Hedvig Kjellström, and Rudolph Triebel · 2020
Later among the works it cites.
A near-optimal algorithm for debiasing trained machine learning models
Ibrahim Alabdulmohsin and Mario Lucic · 2021
Later among the works it cites.
Deep learning through the lens of example difficulty
Robert JN Baldock, Hartmut Maennel, and Behnam Neyshabur · 2021
Later among the works it cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
Later among the works it cites.
Calibration of neural networks using splines
Kartik Gupta, Amir Rahimi, Thalaiyasingam Ajanthan, Thomas Mensink, Cristian Sminchisescu, and Richard Hartley · 2021
Later among the works it cites.
Training independent subnetworks for robust prediction
Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M Dai, and Dustin Tran · 2021
Later among the works it cites.
Soft calibration objectives for neural networks
Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C Mozer, and Becca Roelofs · 2021
Later among the works it cites.
Second opinion needed: communicating uncertainty in medical machine learning
Benjamin Kompa, Jasper Snoek, and Andrew L Beam · 2021
Later among the works it cites.
Uncertainty Baselines: Benchmarks for uncertainty & robustness in deep learning
Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, and Dustin Tran · 2021
Later among the works it cites.
Multi-class uncertainty calibration via mutual information maximization-based binning
Kanil Patel, William Beluch, Bin Yang, Michael Pfeiffer, and Dan Zhang · 2021
Later among the works it cites.
Head2Toe: Utilizing intermediate representations for better transfer learning
Utku Evci, Vincent Dumoulin, Hugo Larochelle, and Michael C Mozer · 2022
Closest in time.