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Bayesian inference was once a gold standard for learning with neural networks, providing accurate full predictive distributions and well calibrated uncertainty.
A simple baseline for bayesian uncertainty in deep learning
Maddox, W., Garipov, T., Izmailov, P., Vetrov, D., and Wilson, A. G. (2019) · 1902
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’In-Between’ Uncertainty in Bayesian Neural Networks
Foong, A. Y. K., Li, Y., Hernández-Lobato, J. M., and Turner, R. E. (2019) · 1906
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Quality of Uncertainty Quantification for Bayesian Neural Network Inference
Yao, J., Pan, W., Ghosh, S., and Doshi-Velez, F. (2019) · 1906
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Annealing markov chain monte carlo with applications to ancestral inference
Geyer, C. J. and Thompson, E. A. (1995) · 1995
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EM algorithms for PCA and SPCA
Roweis, S. T. (1998) · 1998
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Bayesian PCA
Bishop, C. M. (1999) · 1999
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Automatic choice of dimensionality for pca
Minka, T. P. (2001) · 2001
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Information theory, inference and learning algorithms
MacKay, D. J. (2003) · 2003
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Slice sampling
Neal, R. M. et al. (2003) · 2003
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Elliptical slice sampling
Murray, I., Prescott Adams, R., and MacKay, D. J. (2010) · 2010
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Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
Halko, N., Martinsson, P.-G., and Tropp, J. A. (2011) · 2011
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Mcmc using hamiltonian dynamics
Neal, R. M. et al. (2011) · 2011
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Stochastic variational inference
Hoffman, M. D., Blei, D. M., Wang, C., and Paisley, J. (2013) · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2013) · 2013
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A widely applicable bayesian information criterion
Watanabe, S. (2013) · 2013
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Gaussian process kernels for pattern discovery and extrapolation
Wilson, A. and Adams, R. (2013) · 2013
Cited alongside, same era.
The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo
Hoffman, M. D. and Gelman, A. (2014) · 2014
Cited alongside, same era.
Weight uncertainty in neural networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D. (2015) · 2015
Cited alongside, same era.
Bayesian compressed regression
Guhaniyogi, R. and Dunson, D. B. (2015) · 2015
Cited alongside, same era.
Variational dropout and the local reparameterization trick
Kingma, D. P., Salimans, T., and Welling, M. (2015) · 2015
Cited alongside, same era.
Bayesian inference via projections
Silva, R. and Kalaitzis, A. (2015) · 2015
Krueger, D., Huang, C.-W., Islam, R., Turner, R., Lacoste, A., and Courville, A. (2017) · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C. (2017) · 2017
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Learning model reparametrizations: Implicit variational inference by fitting mcmc distributions
Titsias, M. K. (2017) · 2017
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Pyro: Deep universal probabilistic programming
Bingham, E., Chen, J. P., Jankowiak, M., Obermeyer, F., Pradhan, N., Karaletsos, T., Singh, R., Szerlip, P., Horsfall, P., and Goodman, N. D. (2018) · 2018
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Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration
Gardner, J., Pleiss, G., Weinberger, K. Q., Bindel, D., and Wilson, A. G. (2018) · 2018
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Cited alongside, same era.
A la carte–learning fast kernels
Yang, Z., Wilson, A., Smola, A., and Song, L. (2015) · 2015
Cited alongside, same era.
Deep gaussian processes for regression using approximate expectation propagation
Bui, T., Hernández-Lobato, D., Hernandez-Lobato, J., Li, Y., and Turner, R. (2016) · 2016
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z. (2016) · 2016
Cited alongside, same era.
Frequent directions: Simple and deterministic matrix sketching
Ghashami, M., Liberty, E., Phillips, J. M., and Woodruff, D. P. (2016) · 2016
Cited alongside, same era.
Coresets for scalable bayesian logistic regression
Huggins, J., Campbell, T., and Broderick, T. (2016) · 2016
Cited alongside, same era.
Deep kernel learning
Wilson, A. G., Hu, Z., Salakhutdinov, R., and Xing, E. P. (2016) · 2016
Cited alongside, same era.
Loss surfaces, mode connectivity, and fast ensembling of dnns
Garipov, T., Izmailov, P., Podoprikhin, D., Vetrov, D. P., and Wilson, A. G. (2018) · 2018
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Averaging weights leads to wider optima and better generalization
Izmailov, P., Podoprikhin, D., Garipov, T., Vetrov, D., and Wilson, A. G. (2018) · 2018
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Probabilistic meta-representations of neural networks
Karaletsos, T., Dayan, P., and Ghahramani, Z. (2018) · 2018
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Constrained bayesian inference through posterior projections
Patra, S. and Dunson, D. B. (2018) · 2018
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Pradier, M. F., Pan, W., Yao, J., Ghosh, S., and Doshi-velez, F. (2018) · 2018
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Deep bayesian bandits showdown
Riquelme, C., Tucker, G., and Snoek, J. (2018) · 2018
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A scalable laplace approximation for neural networks
Ritter, H., Botev, A., and Barber, D. (2018) · 2018
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Orthogonally decoupled variational gaussian processes
Salimbeni, H., Cheng, C.-A., Boots, B., and Deisenroth, M. (2018) · 2018
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Gradient descent happens in a tiny subspace
Gur-Ari, G., Roberts, D. A., and Dyer, E. (2019) · 2019
Closest in time.
Deterministic Variational Inference for Robust Bayesian Neural Networks
Wu, A., Nowozin, S., Meeds, E., Turner, R. E., Hernández-Lobato, J. M., and Gaunt, A. L. (2019) · 2019
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