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The question whether inputs are valid for the problem a neural network is trying to solve has sparked interest in out-of-distribution (OOD) detection.
Outliers in statistical data
Barnett, V. and Lewis, T · 1984
Earlier work this paper cites.
A theory of the learnable
Valiant, L. G · 1984
Earlier work this paper cites.
Hybrid monte carlo
Duane, S., Kennedy, A. D., Pendleton, B. J., and Roweth, D · 1987
Earlier work this paper cites.
Radial basis functions, multi-variable functional interpolation and adaptive networks
Broomhead, D. S. and Lowe, D · 1988
Earlier work this paper cites.
Approximation capabilities of multilayer feedforward networks
Hornik, K · 1991
Earlier work this paper cites.
Self-improving reactive agents based on reinforcement learning, planning and teaching
Lin, L.-J · 1992
Earlier work this paper cites.
Bayesian Interpolation , pp. 39–66
MacKay, D. J. C · 1992
Earlier work this paper cites.
Bayesian Learning for Neural Networks
Neal, R. M · 1996
Earlier work this paper cites.
Computing with infinite networks
Williams, C. K · 1997
Earlier work this paper cites.
Some pac-bayesian theorems
McAllester, D. A · 1999
Earlier work this paper cites.
Information Theory, Inference and Learning Algorithms
MacKay, D. J. C · 2003
Earlier work this paper cites.
Pac-bayesian generalisation error bounds for gaussian process classification
Seeger, M · 2003
Earlier work this paper cites.
Bayesian statistics: principles and benefits
O’Hagan, A · 2004
Earlier work this paper cites.
Gaussian Processes in Machine Learning , pp. 63–71
Rasmussen, C. E · 2004
Earlier work this paper cites.
Pac-bayesian supervised classification: The thermodynamics of statistical learning
Catoni, O · 2007
Earlier work this paper cites.
Kernel methods for deep learning
Cho, Y. and Saul, L · 2009
Earlier work this paper cites.
Dataset shift in machine learning
Quiñonero-Candela, J., Sugiyama, M., Lawrence, N. D., and Schwaighofer, A · 2009
Earlier work this paper cites.
Mcmc using hamiltonian dynamics
Neal, R. M. et al · 2011
Earlier work this paper cites.
A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A · 2012
Earlier work this paper cites.
A review of novelty detection
Pimentel, M. A., Clifton, D. A., Clifton, L., and Tarassenko, L · 2014
Earlier work this paper cites.
Pac-bayesian theory meets bayesian inference
Germain, P., Bach, F., Lacoste, A., and Lacoste-Julien, S · 2016
Cited alongside, same era.
A kernelized stein discrepancy for goodness-of-fit tests
Liu, Q., Lee, J., and Jordan, M · 2016
Cited alongside, same era.
Mapping gaussian process priors to bayesian neural networks
Flam-Shepherd, D., Requeima, J., and Duvenaud, D · 2017
Cited alongside, same era.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D. and Gimpel, K · 2017
Cited alongside, same era.
Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., Hassabis, D., Clopath, C., Kumaran, D., and Hadsell, R · 2017
Cited alongside, same era.
Bayesian hypernetworks
Krueger, D., Huang, C.-W., Islam, R., Turner, R., Lacoste, A., and Courville, A · 2017
Normalizing flows for probabilistic modeling and inference
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B · 2019
Later among the works it cites.
Continual lifelong learning with neural networks: A review
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., and Wermter, S · 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.
Functional variational bayesian neural networks
Sun, S., Zhang, G., Shi, J., and Grosse, R · 2019
Later among the works it cites.
Conservative uncertainty estimation by fitting prior networks
Ciosek, K., Fortuin, V., Tomioka, R., Hofmann, K., and Turner, R · 2020
Later among the works it cites.
Being bayesian, even just a bit, fixes overconfidence in relu networks
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Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
Louizos, C. and Welling, M · 2017
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Implicit weight uncertainty in neural networks
Pawlowski, N., Brock, A., Lee, M. C., Rajchl, M., and Glocker, B · 2017
Cited alongside, same era.
Continual Learning with Deep Generative Replay
Shin, H., Lee, J. K., Kim, J., and Kim, J · 2017
Cited alongside, same era.
Continual Learning Through Synaptic Intelligence
Zenke, F., Poole, B., and Ganguli, S · 2017
Cited alongside, same era.
Gaussian process behaviour in wide deep neural networks
de G. Matthews, A. G., Hron, J., Rowland, M., Turner, R. E., and Ghahramani, Z · 2018
Cited alongside, same era.
A unifying bayesian view of continual learning
Farquhar, S. and Gal, Y · 2018
Cited alongside, same era.
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Pearce, T., Tsuchida, R., Zaki, M., Brintrup, A., and Neely, A · 2020
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All you need is a good functional prior for bayesian deep learning
Tran, B.-H., Rossi, S., Milios, D., and Filippone, M · 2020
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Continual learning with hypernetworks
von Oswald, J., Henning, C., Sacramento, J., and Grewe, B. F · 2020
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Bayesian deep learning and a probabilistic perspective of generalization
Wilson, A. G. and Izmailov, P · 2020
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Regflow: Probabilistic flow-based regression for future prediction
Zięba, M., Przewięźlikowski, M., Śmieja, M., Tabor, J., Trzcinski, T., and Spurek, P · 2020
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Burt, D. R., Ober, S. W., Garriga-Alonso, A., and van der Wilk, M · 2021
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