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The linearised Laplace method for estimating model uncertainty has received renewed attention in the Bayesian deep learning community.
Bayesian Methods for Adaptive Models
Mackay, D. J. C · 1992
Earlier work this paper cites.
Keeping the neural networks simple by minimizing the description length of the weights
Hinton, G. E. and van Camp, D · 1993
Earlier work this paper cites.
Bayesian Learning for Neural Networks
Neal, R. M · 1995
Earlier work this paper cites.
Effiicient backprop
LeCun, Y., Bottou, L., Orr, G. B., and Müller, K.-R · 1996
Earlier work this paper cites.
Adaptive method of realizing natural gradient learning for multilayer perceptrons
Amari, S., Park, H., and Fukumizu, K · 2000
Earlier work this paper cites.
Variational inference in probabilistic models
Lawrence, N. D · 2000
Earlier work this paper cites.
Annealed importance sampling
Neal, R. M · 2001
Earlier work this paper cites.
Information theory, inference, and learning algorithms
MacKay, D. J. C · 2003
Earlier work this paper cites.
An algorithm for total variation minimization and applications
Chambolle, A · 2004
Earlier work this paper cites.
Pattern recognition and machine learning
Bishop, C. M · 2006
Earlier work this paper cites.
Gaussian processes for machine learning
Rasmussen, C. E. and Williams, C. K. I · 2006
Earlier work this paper cites.
Accelerated conjugate gradient algorithm with finite difference hessian/vector product approximation for unconstrained optimization
Andrei, N · 2008
Earlier work this paper cites.
Practical variational inference for neural networks
Graves, A · 2011
Earlier work this paper cites.
Bayesian learning via stochastic gradient langevin dynamics
Welling, M. and Teh, Y. W · 2011
Earlier work this paper cites.
Stochastic gradient hamiltonian monte carlo
Chen, T., Fox, E. B., and Guestrin, C · 2014
Earlier work this paper cites.
The fundamental incompatibility of scalable hamiltonian monte carlo and naive data subsampling
Betancourt, M · 2015
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.
Probabilistic backpropagation for scalable learning of Bayesian neural networks
Hernández-Lobato, J. M. and Adams, R. P · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
Earlier work this paper cites.
Optimizing neural networks with kronecker-factored approximate curvature
Martens, J. and Grosse, R. B · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
Earlier work this paper cites.
Ba, L. J., Kiros, J. R., and Hinton, G. E · 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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Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Black-box alpha divergence minimization
Hernández-Lobato, J. M., Li, Y., Rowland, M., Bui, T. D., Hernández-Lobato, D., and Turner, R. E · 2016
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Practical Gauss-Newton optimisation for deep learning
Botev, A., Ritter, H., and Barber, D · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
Pitfalls of in-domain uncertainty estimation and ensembling in deep learning
Ashukha, A., Lyzhov, A., Molchanov, D., and Vetrov, D. P · 2020
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Efficient and scalable bayesian neural nets with rank-1 factors
Dusenberry, M. W., Jerfel, G., Wen, Y., Ma, Y.-A., Snoek, J., Heller, K., Lakshminarayanan, B., and Tran, D · 2020
Later among the works it cites.
Being bayesian, even just a bit, fixes overconfidence in relu networks
Kristiadi, A., Hein, M., and Hennig, P · 2020
Later among the works it cites.
Reconciling modern deep learning with traditional optimization analyses: The intrinsic learning rate
Li, Z., Lyu, K., and Arora, S · 2020
Later among the works it cites.
Rethinking parameter counting in deep models: Effective dimensionality revisited
Maddox, W. J., Benton, G. W., and Wilson, A. G · 2020
Later among the works it cites.
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L2 regularization versus batch and weight normalization
van Laarhoven, T · 2017
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Deep learning for classical japanese literature, 2018
Clanuwat, T., Bober-Irizar, M., Kitamoto, A., Lamb, A., Yamamoto, K., and Ha, D · 2018
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Norm matters: efficient and accurate normalization schemes in deep networks
Hoffer, E., Banner, R., Golan, I., and Soudry, D · 2018
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A scalable laplace approximation for neural networks
Ritter, H., Botev, A., and Barber, D · 2018
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Deep image prior
Ulyanov, D., Vedaldi, A., and Lempitsky, V. S · 2018
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Disentangling and learning robust representations with natural clustering
Antoran, J. and Miguel, A · 2019
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How good is the bayes posterior in deep neural networks really?
Wenzel, F., Roth, K., Veeling, B. S., Swiatkowski, J., Tran, L., Mandt, S., Snoek, J., Salimans, T., Jenatton, R., and Nowozin, S · 2020
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Group normalization
Wu, Y. and He, K · 2020
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Getting a CLUE: A method for explaining uncertainty estimates
Antorán, J., Bhatt, U., Adel, T., Weller, A., and Hernández-Lobato, J. M · 2021
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Uncertainty as a form of transparency: Measuring, communicating, and using uncertainty
Bhatt, U., Antorán, J., Zhang, Y., Liao, Q. V., Sattigeri, P., Fogliato, R., Melançon, G. G., Krishnan, R., Stanley, J., Tickoo, O., Nachman, L., Chunara, R., Srikumar, M., Weller, A., and Xiang, A · 2021
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Characterizing signal propagation to close the performance gap in unnormalized resnets
Brock, A., De, S., and Smith, S. L · 2021
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High-performance large-scale image recognition without normalization
Brock, A., De, S., Smith, S. L., and Simonyan, K · 2021
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Scalable marginal likelihood estimation for model selection in deep learning
Immer, A., Bauer, M., Fortuin, V., Rätsch, G., and Khan, M. E · 2021
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Improving predictions of bayesian neural nets via local linearization
Immer, A., Korzepa, M., and Bauer, M · 2021
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What are bayesian neural network posteriors really like?
Izmailov, P., Vikram, S., Hoffman, M. D., and Wilson, A. G. G · 2021
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On the periodic behavior of neural network training with batch normalization and weight decay
Lobacheva, E., Kodryan, M., Chirkova, N., Malinin, A., and Vetrov, D. P · 2021
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Fast adaptation with linearized neural networks
Maddox, W., Tang, S., Moreno, P. G., Wilson, A. G., and Damianou, A · 2021
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Data augmentation in bayesian neural networks and the cold posterior effect
Nabarro, S., Ganev, S., Garriga-Alonso, A., Fortuin, V., van der Wilk, M., and Aitchison, L · 2021
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Global inducing point variational posteriors for bayesian neural networks and deep gaussian processes
Ober, S. W. and Aitchison, L · 2021
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Collapsed variational bounds for bayesian neural networks
Tomczak, M., Swaroop, S., Foong, A., and Turner, R · 2021
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A probabilistic deep image prior for computational tomography
Antorán, J., Barbano, R., Leuschner, J., Hernández-Lobato, J. M., and Jin, B · 2022
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