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We perform scalable approximate inference in continuous-depth Bayesian neural networks.
Fixup initialization: Residual learning without normalization
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Scalable gradients for stochastic differential equations
Li, X., Wong, T.-K. L., Chen, R. T., and Duvenaud, D. (2020) · 2001
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Stochasticity in neural odes: An empirical study
Oganesyan, V., Volokhova, A., and Vetrov, D. (2020) · 2002
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Neural controlled differential equations for irregular time series
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Variational inference for diffusion processes
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Sde-net: Equipping deep neural networks with uncertainty estimates
Kong, L., Sun, J., and Zhang, C. (2020) · 2008
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Practical variational inference for neural networks
Graves, A. (2011) · 2011
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Bayesian neural ordinary differential equations
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The mnist database of handwritten digit images for machine learning research
Deng, L. (2012) · 2012
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Stochastic variational inference
Multiplicative normalizing flows for variational bayesian neural networks
Louizos, C. and Welling, M. (2017) · 2017
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Sticking the landing: Simple, lower-variance gradient estimators for variational inference
Roeder, G., Wu, Y., and Duvenaud, D. K. (2017) · 2017
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Neural ordinary differential equations
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Deep learning with differential gaussian process flows
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Hoffman, M. D., Blei, D. M., Wang, C., and Paisley, J. (2013) · 2013
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Avoiding pathologies in very deep networks
Duvenaud, D., Rippel, O., Adams, R. P., and Ghahramani, Z. (2014) · 2014
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The cifar-10 dataset
Krizhevsky, A., Nair, V., and Hinton, G. (2014) · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D. (2014) · 2014
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Bayesian dark knowledge
Balan, A. K., Rathod, V., Murphy, K. P., and Welling, M. (2015) · 2015
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Weight uncertainty in neural networks
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C. (2015) · 2015
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Mishkin, A., Kunstner, F., Nielsen, D., Schmidt, M., and Khan, M. E. (2018) · 2018
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Black-box variational inference for stochastic differential equations
Ryder, T., Golightly, A., McGough, A. S., and Prangle, D. (2018) · 2018
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Adversarial distillation of bayesian neural network posteriors
Wang, K.-C., Vicol, P., Lucas, J., Gu, L., Grosse, R., and Zemel, R. (2018) · 2018
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Noisy natural gradient as variational inference
Zhang, G., Sun, S., Duvenaud, D., and Grosse, R. (2018) · 2018
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Augmented neural odes
Dupont, E., Doucet, A., and Teh, Y. W. (2019) · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T. (2019) · 2019
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Variational inference for stochastic differential equations
Opper, M. (2019) · 2019
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Principled weight initialization for hypernetworks
Chang, O., Flokas, L., and Lipson, H. (2020) · 2020
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Efficient and scalable bayesian neural nets with rank-1 factors
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Learning differential equations that are easy to solve
Kelly, J., Bettencourt, J., Johnson, M. J., and Duvenaud, D. (2020) · 2020
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How good is the bayes posterior in deep neural networks really?
Wenzel, F., Roth, K., Veeling, B., Swiatkowski, J., Tran, L., Mandt, S., Snoek, J., Salimans, T., Jenatton, R., and Nowozin, S. (2020) · 2020
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What are bayesian neural network posteriors really like?
Izmailov, P., Vikram, S., Hoffman, M. D., and Wilson, A. G. (2021) · 2021
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Neural sdes as infinite-dimensional gans
Kidger, P., Foster, J., Li, X., Oberhauser, H., and Lyons, T. (2021) · 2021
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