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Uncertainty quantification is a fundamental yet unsolved problem for deep learning.
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Deep residual learning for image recognition
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Stochastic differential equations
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Structured and efficient variational deep learning with matrix gaussian posteriors
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Chen, T. Q., Rubanova, Y., Bettencourt, J., and Duvenaud, D. K · 2018
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Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
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Malinin, A. and Gales, M · 2019
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Neural stochastic differential equations: Deep latent gaussian models in the diffusion limit
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Deterministic variational inference for robust bayesian neural networks
Wu, A., Nowozin, S., Meeds, E., Turner, R. E., Hernandez-Lobato, J. M., and Gaunt, A. L · 2019
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Scalable gradients for stochastic differential equations
Li, X., Wong, T.-K. L., Chen, R. T., and Duvenaud, D · 2020
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