Fetching the paper…
Reading the bibliography…
Sampling-based methods, e.g., Deep Ensembles and Bayesian Neural Nets have become promising approaches to improve the quality of uncertainty estimation and robust generalization.
Verification of Forecasts Expressed in Terms of Probability
Brier, G. W · 1950
Earlier work this paper cites.
Geometric measure theory
Federer, H · 1969
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.
Statistical Learning Theory
Vapnik, V. N · 1998
Earlier work this paper cites.
Game theory, maximum entropy, minimum discrepancy and robust Bayesian decision theory
Grünwald, P. D. and Dawid, A. P · 2004
Earlier work this paper cites.
Metric Spaces
Searcód, M. O · 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.
Reliability, sufficiency, and the decomposition of proper scores
Bröcker, J · 2009
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.
Proper local scoring rules
Parry, M., Dawid, A. P., and Lauritzen, S · 2012
Earlier work this paper cites.
Weight uncertainty in neural networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Earlier work this paper cites.
Obtaining well calibrated probabilities using bayesian binning
Naeini, M. P., Cooper, G. F., and Hauskrecht, M · 2015
Earlier work this paper cites.
Scalable bayesian optimization using deep neural networks
Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N., Sundaram, N., Patwary, M., Prabhat, M., and Adams, R · 2015
Earlier work this paper cites.
Deep Learning
Goodfellow, I. J., Bengio, Y., and Courville, A · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Wide residual networks
Zagoruyko, S. and Komodakis, N · 2016
Earlier work this paper cites.
Density estimation using real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2017
Earlier work this paper cites.
Concrete dropout
Gal, Y., Hron, J., and Kendall, A · 2017
Earlier work this paper cites.
Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 2017
Earlier work this paper cites.
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
Earlier work this paper cites.
Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration
Gardner, J., Pleiss, G., Weinberger, K. Q., Bindel, D., and Wilson, A. G · 2018
Earlier work this paper cites.
Accurate uncertainties for deep learning using calibrated regression
Kuleshov, V., Fenner, N., and Ermon, S · 2018
Earlier work this paper cites.
Predictive uncertainty estimation via prior networks
Malinin, A. and Gales, M · 2018
Cited alongside, same era.
Spectral normalization for generative adversarial networks
Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y · 2018
Cited alongside, same era.
Do cifar-10 classifiers generalize to cifar-10?, 2018
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 2018
Cited alongside, same era.
Deep bayesian bandits showdown: An empirical comparison of bayesian deep networks for thompson sampling
Riquelme, C., Tucker, G., and Snoek, J · 2018
Cited alongside, same era.
Evidential deep learning to quantify classification uncertainty
Sensoy, M., Kaplan, L., and Kandemir, M · 2018
Cited alongside, same era.
Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks
Tsuzuku, Y., Sato, I., and Sugiyama, M · 2018
Batchensemble: an alternative approach to efficient ensemble and lifelong learning
Wen, Y., Tran, D., and Ba, J · 2020
Later among the works it cites.
Exploiting domain-specific features to enhance domain generalization
Bui, M.-H., Tran, T., Tran, A., and Phung, D · 2021
Later among the works it cites.
Correlated input-dependent label noise in large-scale image classification
Collier, M., Mustafa, B., Kokiopoulou, E., Jenatton, R., and Berent, J · 2021
Later among the works it cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2021
Later among the works it cites.
Regularisation of neural networks by enforcing lipschitz continuity
Gouk, H., Frank, E., Pfahringer, B., and Cree, M. J · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Lipschitz regularity of deep neural networks: analysis and efficient estimation
Virmaux, A. and Scaman, K · 2018
Cited alongside, same era.
Flipout: Efficient pseudo-independent weight perturbations on mini-batches
Wen, Y., Vicol, P., Ba, J., Tran, D., and Grosse, R · 2018
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2018
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
Cited alongside, same era.
Preventing gradient attenuation in lipschitz constrained convolutional networks
Li, Q., Haque, S., Anil, C., Lucas, J., Grosse, R. B., and Jacobsen, J.-H · 2019
Cited alongside, same era.
A simple baseline for bayesian uncertainty in deep learning
Maddox, W. J., Izmailov, P., Garipov, T., Vetrov, D. P., and Wilson, A. G · 2019
Cited alongside, same era.
Havasi, M., Jenatton, R., Fort, S., Liu, J. Z., Snoek, J., Lakshminarayanan, B., Dai, A. M., and Tran, D · 2021
Later among the works it cites.
Soft calibration objectives for neural networks
Karandikar, A., Cain, N., Tran, D., Lakshminarayanan, B., Shlens, J., Mozer, M. C., and Roelofs, B · 2021
Later among the works it cites.
Evaluating robustness of predictive uncertainty estimation: Are dirichlet-based models reliable ?
Kopetzki, A.-K., Charpentier, B., Zügner, D., Giri, S., and Günnemann, S · 2021
Later among the works it cites.
Revisiting the calibration of modern neural networks
Minderer, M., Djolonga, J., Romijnders, R., Hubis, F., Zhai, X., Houlsby, N., Tran, D., and Lucic, M · 2021
Later among the works it cites.
Uncertainty Baselines: Benchmarks for uncertainty & robustness in deep learning
Nado, Z., Band, N., Collier, M., Djolonga, J., Dusenberry, M., Farquhar, S., Filos, A., Havasi, M., Jenatton, R., Jerfel, G., Liu, J., Mariet, Z., Nixon, J., Padhy, S., Ren, J., Rudner, T., Wen, Y., Wenzel, F., Murphy, K., Sculley, D., Lakshminarayanan, B., Snoek, J., Gal, Y., and Tran, D · 2021
Later among the works it cites.
Normalizing flows for probabilistic modeling and inference
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B · 2021
Later among the works it cites.
Pay attention to your loss : understanding misconceptions about lipschitz neural networks
Béthune, L., Boissin, T., Serrurier, M., Mamalet, F., Friedrich, C., and Gonzalez Sanz, A · 2022
Later among the works it cites.
Benchmark for uncertainty & robustness in self-supervised learning, 2022
Bui, H. M. and Maifeld-Carucci, I · 2022
Later among the works it cites.
On mixup regularization
Carratino, L., Cissé, M., Jenatton, R., and Vert, J.-P · 2022
Later among the works it cites.
Natural posterior network: Deep bayesian predictive uncertainty for exponential family distributions
Charpentier, B., Borchert, O., Zügner, D., Geisler, S., and Günnemann, S · 2022
Later among the works it cites.
A close look into the calibration of pre-trained language models, 2022
Chen, Y., Yuan, L., Cui, G., Liu, Z., and Ji, H · 2022
Later among the works it cites.
Nonparametric uncertainty quantification for single deterministic neural network
Kotelevskii, N., Artemenkov, A., Fedyanin, K., Noskov, F., Fishkov, A., Shelmanov, A., Vazhentsev, A., Petiushko, A., and Panov, M · 2022
Later among the works it cites.
Calibrated and sharp uncertainties in deep learning via density estimation
Kuleshov, V. and Deshpande, S · 2022
Later among the works it cites.
Out-of-distribution detection with deep nearest neighbors
Sun, Y., Ming, Y., Zhu, X., and Li, Y · 2022
Later among the works it cites.
Plex: Towards reliability using pretrained large model extensions, 2022
Tran, D., Liu, J., Dusenberry, M. W., Phan, D., Collier, M., Ren, J., Han, K., Wang, Z., Mariet, Z., Hu, H., Band, N., Rudner, T. G. J., Singhal, K., Nado, Z., van Amersfoort, J., Kirsch, A., Jenatton, R., Thain, N., Yuan, H., Buchanan, K., Murphy, K., Sculley, D., Gal, Y., Ghahramani, Z., Snoek, J., and Lakshminarayanan, B · 2022
Later among the works it cites.
On feature collapse and deep kernel learning for single forward pass uncertainty, 2022
van Amersfoort, J., Smith, L., Jesson, A., Key, O., and Gal, Y · 2022
Later among the works it cites.
Vim: Out-of-distribution with virtual-logit matching
Wang, H., Li, Z., Feng, L., and Zhang, W · 2022
Later among the works it cites.
Mitigating neural network overconfidence with logit normalization
Wei, H., Xie, R., Cheng, H., Feng, L., An, B., and Li, Y · 2022
Later among the works it cites.
Deep deterministic uncertainty: A new simple baseline
Mukhoti, J., Kirsch, A., van Amersfoort, J., Torr, P. H., and Gal, Y · 2023
Closest in time.
Density-regression: Efficient and distance-aware deep regressor for uncertainty estimation under distribution shifts
Manh Bui, H. and Liu, A · 2024
Closest in time.