Fetching the paper…
Reading the bibliography…
This paper studies the problem of post-hoc calibration of machine learning classifiers.
The choice of a class interval
Sturges, H. A · 1926
Earlier work this paper cites.
An empirical distribution function for sampling with incomplete information
Ayer, M., Brunk, H. D., Ewing, G. M., Reid, W. T., and Silverman, E · 1955
Earlier work this paper cites.
Remarks on some nonparametric estimates of a density function
Rosenblatt, M · 1956
Earlier work this paper cites.
On estimation of a probability density function and model
Parzen, E · 1962
Earlier work this paper cites.
A new vector partition of the probability score
Murphy, A. H · 1973
Earlier work this paper cites.
The well-calibrated bayesian
Dawid, A. P · 1982
Earlier work this paper cites.
On the method of bounded differences
McDiarmid, C · 1989
Earlier work this paper cites.
Multivariate density estimation
Scott, D · 1992
Earlier work this paper cites.
Measuring the stability of histogram appearance when the anchor position is changed
Simonoff, J. S. and Udina, F · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al · 1998
Earlier work this paper cites.
On the use of density kernels for concentration estimations within particle and puff dispersion models
de Haan, P · 1999
Earlier work this paper cites.
Probabilistic outputs for support vector machines and comparison to regularized likelihood methods
Platt, J · 2000
Earlier work this paper cites.
Learning and making decisions when costs and probabilities are both unknown
Zadrozny, B. and Elkan, C · 2001
Earlier work this paper cites.
Transforming classifier scores into accurate multiclass probability estimates
Zadrozny, B. and Elkan, C · 2002
Earlier work this paper cites.
Loss functions for binary class probability estimation and classification: Structure and applications
Buja, A., Stuetzle, W., and Shen, Y · 2005
Earlier work this paper cites.
Predicting good probabilities with supervised learning
Niculescu-Mizil, A. and Caruana, R · 2005
Earlier work this paper cites.
A distribution-free theory of nonparametric regression
Györfi, L., Kohler, M., Krzyzak, A., and Walk, H · 2006
Earlier work this paper cites.
Strictly proper scoring rules, prediction, and estimation
Gneiting, T. and Raftery, A. E · 2007
Earlier work this paper cites.
Introduction to Nonparametric Estimation
Tsybakov, A. B · 2008
Earlier work this paper cites.
Reliability, sufficiency, and the decomposition of proper scores
Bröcker, J · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Cited alongside, same era.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
Cited alongside, same era.
Weight uncertainty in neural networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
Cited alongside, same era.
Deepdriving: Learning affordance for direct perception in autonomous driving
Chen, C., Seff, A., Kornhauser, A., and Xiao, J · 2015
Cited alongside, same era.
Fast r-cnn
Girshick, R · 2015
Cited alongside, same era.
Obtaining well calibrated probabilities using bayesian binning
Naeini, M., Cooper, G., and Hauskrecht, M · 2015
Cited alongside, same era.
To trust or not to trust a classifier
Jiang, H., Kim, B., Guan, M., and Gupta, M · 2018
Later among the works it cites.
Trainable calibration measures for neural networks from kernel mean embeddings
Kumar, A., Sarawagi, S., and Jain, U · 2018
Later among the works it cites.
Dirichlet-based gaussian processes for large-scale calibrated classification
Milios, D., Camoriano, R., Michiardi, P., Rosasco, L., and Filippone, M · 2018
Later among the works it cites.
Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem
Hein, M., Andriushchenko, M., and Bitterwolf, J · 2019
Later among the works it cites.
Deep anomaly detection with outlier exposure
Hendrycks, D., Mazeika, M., and Dietterich, T · 2019
Later among the works it cites.
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with dirichlet calibration
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., and Clune, J · 2015
Cited alongside, same era.
Exact rate of convergence of kernel-based classification rule
Döring, M., Györfi, L., and Walk, H · 2016
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
Cited alongside, same era.
Wide residual networks
Zagoruyko, S. and Komodakis, N · 2016
Cited alongside, same era.
Kull, M., Nieto, M. P., Kängsepp, M., Silva Filho, T., Song, H., and Flach, P · 2019
Later among the works it cites.
Verified uncertainty calibration
Kumar, A., Liang, P. S., and Ma, T · 2019
Later among the works it cites.
A simple baseline for bayesian uncertainty in deep learning
Maddox, W. J., Izmailov, P., Garipov, T., Vetrov, D. P., and Wilson, A. G · 2019
Later among the works it cites.
When does label smoothing help?
Müller, R., Kornblith, S., and Hinton, G. E · 2019
Later among the works it cites.
Measuring calibration in deep learning
Nixon, J., Dusenberry, M. W., Zhang, L., Jerfel, G., and Tran, D · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
Later among the works it cites.
Learning for single-shot confidence calibration in deep neural networks through stochastic inferences
Seo, S., Seo, P. H., and Han, B · 2019
Later among the works it cites.
Calibrating deep convolutional gaussian processes
Tran, G.-L., Bonilla, E., Cunningham, J., Michiardi, P., and Filippone, M · 2019
Later among the works it cites.
Evaluating model calibration in classification
Vaicenavicius, J., Widmann, D., Andersson, C., Lindsten, F., Roll, J., and Schön, T · 2019
Later among the works it cites.
Calibration tests in multi-class classification: A unifying framework
Widmann, D., Lindsten, F., and Zachariah, D · 2019
Later among the works it cites.
Pitfalls of in-domain uncertainty estimation and ensembling in deep learning
Ashukha, A., Lyzhov, A., Molchanov, D., and Vetrov, D · 2020
Closest in time.
Revisiting the evaluation of uncertainty estimation and its application to explore model complexity-uncertainty trade-off
Ding, Y., Liu, J., Xiong, J., and Shi, Y · 2020
Closest in time.
Intra order-preserving functions for calibration of multi-class neural networks
Rahimi, A., Shaban, A., Cheng, C.-A., Boots, B., and Hartley, R · 2020
Closest in time.
Non-parametric calibration for classification
Wenger, J., Kjellström, H., and Triebel, R · 2020
Closest in time.