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We describe Bayesian Layers, a module designed for fast experimentation with neural network uncertainty.
Software for flexible bayesian modeling and markov chain sampling
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TensorFlow: Large-scale machine learning on heterogeneous systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X. (2015) · 2015
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MXNet: A flexible and efficient machine learning library for heterogeneous distributed systems
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Made: Masked autoencoder for distribution estimation
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Probabilistic backpropagation for scalable learning of bayesian neural networks
Hernández-Lobato, J. M. and Adams, R. P. (2015) · 2015
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Variational inference with normalizing flows
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Theano: A Python framework for fast computation of mathematical expressions
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Multiplicative normalizing flows for variational bayesian neural networks
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GPflow: A Gaussian process library using TensorFlow
Matthews, A. G. d. G., van der Wilk, M., Nickson, T., Fujii, K., Boukouvalas, A., León-Villagrá, P., Ghahramani, Z., and Hensman, J. (2017) · 2017
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Probtorch
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Salimans, T., Karpathy, A., Chen, X., and Kingma, D. P. (2017) · 2017
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Doubly stochastic variational inference for deep gaussian processes
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