Fetching the paper…
Reading the bibliography…
Accurate estimation of predictive uncertainty in modern neural networks is critical to achieve well calibrated predictions and detect out-of-distribution (OOD) inputs.
Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 1903
Earlier work this paper cites.
The comparison and evaluation of forecasters
DeGroot, M. H. and Fienberg, S. E · 1983
Earlier work this paper cites.
Bayesian methods for adaptive models
MacKay, D. J · 1992
Earlier work this paper cites.
Multi-category classification by soft-max combination of binary classifiers
Duan, K., Keerthi, S. S., Chu, W., Shevade, S. K., and Poo, A. N · 2003
Earlier work this paper cites.
In defense of one-vs-all classification
Rifkin, R. and Klautau, A · 2004
Earlier work this paper cites.
One-against-all multi-class svm classification using reliability measures
Liu, Y. and Zheng, Y. F · 2005
Earlier work this paper cites.
Predicting good probabilities with supervised learning
Niculescu-Mizil, A. and Caruana, R · 2005
Earlier work this paper cites.
Image classification using svms: one-against-one vs one-against-all
Anthony, G., Gregg, H., and Tshilidzi, M · 2007
Earlier work this paper cites.
Strictly proper scoring rules, prediction, and estimation
Gneiting, T. and Raftery, A. E · 2007
Earlier work this paper cites.
Multiclass and binary svm classification: Implications for training and classification users
Mathur, A. and Foody, G. M · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
Earlier work this paper cites.
Composite binary losses
Reid, M. D. and Williamson, R. C · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
Earlier work this paper cites.
Bayesian learning for neural networks , volume 118
Neal, R. M · 2012
Earlier work this paper cites.
Weight uncertainty in neural networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al · 2016
Cited alongside, same era.
Concrete problems in AI safety
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., and Mané, D · 2016
Cited alongside, same era.
End to end learning for self-driving cars
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., et al · 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.
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Lee, K., Lee, K., Lee, H., and Shin, J · 2018
Later among the works it cites.
Simple, distributed, and accelerated probabilistic programming
Tran, D., Hoffman, M. D., Moore, D., Suter, C., Vasudevan, S., Radul, A., Johnson, M., and Saurous, R. A · 2018
Later among the works it cites.
Deep-RBF networks revisited: Robust classification with rejection
Zadeh, P. H., Hosseini, R., and Sra, S · 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.
An evaluation dataset for intent classification and out-of-scope prediction
Larson, S., Mahendran, A., Peper, J. J., Clarke, C., Lee, A., Hill, P., Kummerfeld, J. K., Leach, K., Laurenzano, M. A., Tang, L., et al · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D. and Gimpel, K · 2016
Cited alongside, same era.
Zagoruyko, S. and Komodakis, N · 2016
Cited alongside, same era.
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., and Thrun, S · 2017
Cited alongside, same era.
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
Cited alongside, same era.
Enhancing the reliability of out-of-distribution image detection in neural networks
Liang, S., Li, Y., and Srikant, R · 2017
Cited alongside, same era.
DOC: Deep open classification of text documents
Shu, L., Xu, H., and Liu, B · 2017
Cited alongside, same era.
Later among the works it cites.
Isotropic maximization loss and entropic score: Fast, accurate, scalable, unexposed, turnkey, and native neural networks out-of-distribution detection
Macêdo, D., Ren, T. I., Zanchettin, C., Oliveira, A. L., Tapp, A., and Ludermir, T · 2019
Later among the works it cites.
A survey on image data augmentation for deep learning
Shorten, C. and Khoshgoftaar, T. M · 2019
Later among the works it cites.
Can you trust your model’s uncertainty? Evaluating predictive uncertainty under dataset shift
Snoek, J., Ovadia, Y., Fertig, E., Lakshminarayanan, B., Nowozin, S., Sculley, D., Dillon, J., Ren, J., and Nado, Z · 2019
Later among the works it cites.
Small and practical bert models for sequence labeling
Tsai, H., Riesa, J., Johnson, M., Arivazhagan, N., Li, X., and Archer, A · 2019
Later among the works it cites.
Distance-based learning from errors for confidence calibration
Xing, C., Arik, S., Zhang, Z., and Pfister, T · 2019
Later among the works it cites.
Analyzing the role of model uncertainty for electronic health records
Dusenberry, M. W., Tran, D., Choi, E., Kemp, J., Nixon, J., Jerfel, G., Heller, K., and Dai, A. M · 2020
Closest in time.
One versus all for deep neural network incertitude (ovnni) quantification
Franchi, G., Bursuc, A., Aldea, E., Dubuisson, S., and Bloch, I · 2020
Closest in time.
The intriguing effects of focal loss on the calibration of deep neural networks, 2020
Mukhoti, J., Kulharia, V., Sanyal, A., Golodetz, S., Torr, P., and Dokania, P · 2020
Closest in time.