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Variational Bayesian Inference is a popular methodology for approximating posterior distributions over Bayesian neural network weights.
A mean field theory learning algorithm for neural networks
Peterson, C · 1987
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A practical bayesian framework for backpropagation networks
MacKay, D. J · 1992
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Keeping neural networks simple by minimizing the description length of the weights
Hinton, G. and Van Camp, D · 1993
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Bayesian learning via stochastic dynamics
Neal, R. M · 1993
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Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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A variational approach to bayesian logistic regression models and their extensions
Jaakkola, T. and Jordan, M · 1997
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Ensemble learning for multi-layer networks
Barber, D. and Bishop, C. M · 1998
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al · 1998
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Bayesian model selection for support vector machines, gaussian processes and other kernel classifiers
Seeger, M · 2000
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Using bayesian model averaging to calibrate forecast ensembles
Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M · 2005
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y · 2010
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MNIST handwritten digit database
LeCun, Y. and Cortes, C · 2010
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Concave gaussian variational approximations for inference in large-scale bayesian linear models
Challis, E. and Barber, D · 2011
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Deep sparse rectifier neural networks
Glorot, X., Bordes, A., and Bengio, Y · 2011
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Practical variational inference for neural networks
Graves, A · 2011
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Learning word vectors for sentiment analysis
Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., and Potts, C · 2011
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Piecewise bounds for estimating bernoulli-logistic latent gaussian models
Marlin, B. M., Khan, M. E., and Murphy, K. P · 2011
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Two problems with variational expectation maximisation for time-series models , pp. 109–130
Turner, R. and Sahani, M · 2011
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Cited alongside, same era.
Fixed-form variational posterior approximation through stochastic linear regression
Salimans, T., Knowles, D. A., et al · 2013
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Cited alongside, same era.
Black box variational inference
Ranganath, R., Gerrish, S., and Blei, D · 2014
Cited alongside, same era.
Doubly stochastic variational bayes for non-conjugate inference
Titsias, M. and Lázaro-Gredilla, M · 2014
Cited alongside, same era.
Variational boosting: Iteratively refining posterior approximations
Miller, A. C., Foti, N. J., and Adams, R. P · 2017
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Learning structured weight uncertainty in bayesian neural networks
Sun, S., Chen, C., and Carin, L · 2017
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Covariances, robustness and variational bayes
Giordano, R., Broderick, T., and Jordan, M. I · 2018
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Matrix variate distributions
Gupta, A. K. and Nagar, D. K · 2018
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Probabilistic meta-representations of neural networks
Karaletsos, T., Dayan, P., and Ghahramani, Z · 2018
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Fast and scalable bayesian deep learning by weight-perturbation in adam
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Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
Cited alongside, same era.
Keras, 2015
Chollet, F. et al · 2015
Cited alongside, same era.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., and Sun, J · 2015
Cited alongside, same era.
Probabilistic backpropagation for scalable learning of bayesian neural networks
Hernández-Lobato, J. M. and Adams, R · 2015
Cited alongside, same era.
Variational dropout and the local reparameterization trick
Kingma, D. P., Salimans, T., and Welling, M · 2015
Cited alongside, same era.
Variational inference with normalizing flows
Rezende, D. and Mohamed, S · 2015
Cited alongside, same era.
Deep learning
Goodfellow, I., Bengio, Y., and Courville, A · 2016
Cited alongside, same era.
Khan, M. E., Nielsen, D., Tangkaratt, V., Lin, W., Gal, Y., and Srivastava, A · 2018
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Slang: Fast structured covariance approximations for bayesian deep learning with natural gradient
Mishkin, A., Kunstner, F., Nielsen, D., Schmidt, M., and Khan, M. E · 2018
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Gaussian variational approximation with a factor covariance structure
Ong, V. M.-H., Nott, D. J., and Smith, M. S · 2018
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Gaussian variational approximation with sparse precision matrices
Tan, L. S. and Nott, D. J · 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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Advances in variational inference
Zhang, C., Butepage, J., Kjellstrom, H., and Mandt, S · 2018
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Noisy natural gradient as variational inference
Zhang, G., Sun, S., Duvenaud, D., and Grosse, R · 2018
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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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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J. V., Lakshminarayanan, B., and Snoek, J · 2019
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Bayesian Layers: A module for neural network uncertainty
Tran, D., Dusenberry, M. W., Hafner, D., and van der Wilk, M · 2019
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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
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How good is the bayes posterior in deep neural networks really?
Wenzel, F., Roth, K., Veeling, B. S., Świątkowski, J., Tran, L., Mandt, S., Snoek, J., Salimans, T., Jenatton, R., and Nowozin, S · 2020
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