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A central tenet of probabilistic programming is that a model is specified exactly once in a canonical representation which is usable by inference algorithms.
Composable effects for flexible and accelerated probabilistic programming in numpyro
Phan, D., Pradhan, N., and Jankowiak, M. (2019) · 1912
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Pep 342–coroutines via enhanced generators
Van Rossum, G. and Eby, P. J. (2005) · 2005
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Probabilistic matrix factorization
Mnih, A. and Salakhutdinov, R. R. (2008) · 2008
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Mcmc using hamiltonian dynamics
Neal, R. M. (2011) · 2011
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The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo
Hoffman, M. D. and Gelman, A. (2014) · 2014
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Black box variational inference
Ranganath, R., Gerrish, S., and Blei, D. (2014) · 2014
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Importance weighted autoencoders
Burda, Y., Grosse, R., and Salakhutdinov, R. (2015) · 2015
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · 2016
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Variational inference for monte carlo objectives
Mnih, A. and Rezende, D. J. (2016) · 2016
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Dillon, J. V., Langmore, I., Tran, D., Brevdo, E., Vasudevan, S., Moore, D., Patton, B., Alemi, A., Hoffman, M., and Saurous, R. A. (2017) · 2017
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Automatic differentiation variational inference
Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., and Blei, D. M. (2017) · 2017
Cited alongside, same era.
JAX: composable transformations of Python+NumPy programs
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., and Wanderman-Milne, S. (2018) · 2018
Doubly reparameterized gradient estimators for monte carlo objectives
Tucker, G., Lawson, D., Gu, S., and Maddison, C. J. (2018) · 2018
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Static automatic batching in tensorflow
Agarwal, A. (2019) · 2019
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Pyro: Deep universal probabilistic programming
Bingham, E., Chen, J. P., Jankowiak, M., Obermeyer, F., Pradhan, N., Karaletsos, T., Singh, R., Szerlip, P., Horsfall, P., and Goodman, N. D. (2019) · 2019
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Gen: a general-purpose probabilistic programming system with programmable inference
Cusumano-Towner, M. F., Saad, F. A., Lew, A. K., and Mansinghka, V. K. (2019) · 2019
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Langevin dynamics as nonparametric variational inference
Hoffman, M. D. and Ma, Y. (2019) · 2019
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Effect handling for composable program transformations in edward2
Moore, D. and Gorinova, M. I. (2018) · 2018
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Simple, distributed, and accelerated probabilistic programming
Tran, D., Hoffman, M. W., Moore, D., Suter, C., Vasudevan, S., and Radul, A. (2018) · 2018
Cited alongside, same era.
Kochurov, M., Carroll, C., Wiecki, T., and Lao, J. (2019) · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · 2019
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