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Bayesian neural networks (BNNs) demonstrate promising success in improving the robustness and uncertainty quantification of modern deep learning.
Efficient estimations from a slowly convergent Robbins-Monro process
Ruppert, D · 1988
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Keeping the neural networks simple by minimizing the description length of the weights
Hinton, G. E. and Van Camp, D · 1993
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Long short-term memory
Schmidhuber, J. and Hochreiter, S · 1997
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Improving the mean field approximation via the use of mixture distributions
Jaakkola, T. S. and Jordan, M. I · 1998
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An introduction to variational methods for graphical models
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Variational inference in probabilistic models
Lawrence, N. D · 2001
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Prior distributions for variance parameters in hierarchical models (comment on article by browne and draper)
Gelman, A. et al · 2006
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Handling sparsity via the horseshoe
Carvalho, C. M., Polson, N. G., and Scott, J. G · 2009
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Li, F.-F · 2009
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The horseshoe estimator for sparse signals
Carvalho, C. M., Polson, N. G., and Scott, J. G · 2010
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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Weight uncertainty in neural networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
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Variational Dropout and the Local Reparameterization Trick
Kingma, D. P., Salimans, T., and Welling, M · 2015
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Generating sentences from a continuous space
Bowman, S. R., Vilnis, L., Vinyals, O., Dai, A. M., Jozefowicz, R., and Bengio, S · 2016
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Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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MIMIC-III, a freely accessible critical care database
Johnson, A. E., Pollard, T. J., Shen, L., Lehman, L.-w. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., and Mark, R. G · 2016
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Structured and efficient variational deep learning with matrix gaussian posteriors
Louizos, C. and Welling, M · 2016
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Hierarchical variational models
Ranganath, R., Tran, D., and Blei, D · 2016
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Zagoruyko, S. and Komodakis, N · 2016
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Model selection in Bayesian neural networks via horseshoe priors
Predictive Uncertainty Estimation via Prior Networks
Malinin, A. and Gales, M · 2018
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Bayesian Layers: A Module for Neural Network Uncertainty
Tran, D., Dusenberry, M. W., van der Wilk, M., and Hafner, D · 2018
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Flipout: Efficient pseudo-independent weight perturbations on mini-batches
Wen, Y., Vicol, P., Ba, J., Tran, D., and Grosse, R · 2018
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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 · 2019
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Deep ensembles: A loss landscape perspective
Fort, S., Hu, H., and Lakshminarayanan, B · 2019
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Ghosh, S. and Doshi-Velez, F · 2017
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Variational inference for sparse and undirected models
Ingraham, J. and Marks, D · 2017
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What uncertainties do we need in Bayesian deep learning for computer vision?
Kendall, A. and Gal, Y · 2017
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Automatic differentiation variational inference
Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., and Blei, D. M · 2017
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
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Multiplicative normalizing flows for variational Bayesian neural networks
Louizos, C. and Welling, M · 2017
Cited alongside, same era.
Bayesian compression for deep learning
Louizos, C., Ullrich, K., and Welling, M · 2017
Cited alongside, same era.
Bayesian inference for large scale image classification
Heek, J. and Kalchbrenner, N · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
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Subspace inference for Bayesian deep learning
Izmailov, P., Maddox, W. J., Kirichenko, P., Garipov, T., Vetrov, D., and Wilson, A. G · 2019
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A simple baseline for Bayesian uncertainty in deep learning
Maddox, W., Garipov, T., Izmailov, P., Vetrov, D., and Wilson, A. G · 2019
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Practical deep learning with Bayesian principles
Osawa, K., Swaroop, S., Jain, A., Eschenhagen, R., Turner, R. E., Yokota, R., and Khan, M. E · 2019
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Improving adversarial robustness via promoting ensemble diversity
Pang, T., Xu, K., Du, C., Chen, N., and Zhu, J · 2019
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Do ImageNet classifiers generalize to ImageNet?
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 2019
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The k-tied normal distribution: A compact parameterization of Gaussian mean field posteriors in Bayesian neural networks
Swiatkowski, J., Roth, K., Veeling, B. S., Tran, L., Dillon, J. V., Snoek, J., Mandt, S., Salimans, T., Jenatton, R., and Nowozin, S · 2019
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BatchEnsemble: An alternative approach to efficient ensemble and lifelong learning
Wen, Y., Tran, D., and Ba, J · 2020
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Cyclical stochastic gradient MCMC for Bayesian deep learning
Zhang, R., Li, C., Zhang, J., Chen, C., and Wilson, A. G · 2020
Closest in time.