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Estimating the gradients of stochastic nodes in stochastic computational graphs is one of the crucial research questions in the deep generative modeling community, which enables the gradient descent optimization on neural network parameters.
Simple statistical gradient-following algorithms for connectionist reinforcement learning,
R. J. Williams, · 1992
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation,
Y. Bengio, N. Leonard, A. Courville, · 2013
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Auto-encoding variational bayes,
D. P. Kingma, M. Welling, · 2014
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Efficient gradient-based inference through transformations between bayes nets and neural nets,
D. P. Kingma, M. Welling, · 2014
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Neural variational inference and learning in belief networks,
A. Mnih, K. Gregor, · 2014
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Deep exponential families,
R. Ranganath, L. Tang, L. Charlin, D. Blei, · 2015
Earlier work this paper cites.
Tensorflow: A system for large-scale machine learning,
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al., · 2016
Earlier work this paper cites.
Deep survival analysis,
R. Ranganath, A. Perotte, N. Elhadad, D. Blei, · 2016
Earlier work this paper cites.
Muprop: Unbiased backpropagation for stochastic neural networks,
S. Gu, S. Levine, I. Sutskever, A. Mnih, · 2016
Earlier work this paper cites.
Variational inference for monte carlo objectives,
A. Mnih, D. J. Rezende, · 2016
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Neural variational inference for text processing,
Y. Miao, L. Yu, P. Blunsom, · 2016
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Stick-breaking variational autoencoders,
E. Nalisnick, P. Smyth, · 2017
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Categorical reparameterization with gumbel-softmax,
E. Jang, S. Gu, B. Poole, · 2017
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The concrete distribution: A continuous relaxation of discrete random variables,
C. J. Maddison, A. Mnih, Y. W. Teh, · 2017
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Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models,
G. Tucker, A. Mnih, C. J. Maddison, J. Lawson, J. Sohl-Dickstein, · 2017
Cited alongside, same era.
Variance reduction properties of the reparameterization trick,
M. Xu, M. Quiroz, R. Kohn, S. A. Sisson, · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library,
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, S. Chintala, · 2019
Later among the works it cites.
Rao-blackwellized stochastic gradients for discrete distributions,
R. Liu, J. Regier, N. Tripuraneni, M. I. Jordan, J. McAuliffe, · 2019
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Dirichlet variational autoencoder,
W. Joo, W. Lee, S. Park, I. C. Moon, · 2020
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Low-variance black-box gradient estimates for the plackett-luce distribution,
A. Gadetsky, K. Struminsky, C. Robinson, N. Quadrianto, D. P. Vetrov, · 2020
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Gradient estimation with stochastic softmax tricks,
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Backpropagation through the void: Optimizing control variates for black-box gradient estimation,
W. Grathwohl, D. Choi, Y. Wu, G. Roeder, D. Duvenaud, · 2017
Cited alongside, same era.
Implicit reparameterization gradients,
M. Figurnov, S. Mohamed, A. Mnih, · 2018
Cited alongside, same era.
Pathwise derivatives beyond the reparameterization trick,
M. Jankowiak, F. Obermeyer, · 2018
Cited alongside, same era.
M. B. Paulus, D. Choi, D. Tarlow, A. Krause, C. J. Maddison, · 2020
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Neural mixed counting models for dispersed topic discovery,
J. Wu, Y. Rao, Z. Zhang, H. Xie, Q. Li, F. L. Wang, Z. Chen, · 2020
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Estimating gradients for discrete random variables by sampling without replacement,
W. Kool, H. van Hoof, M. Welling, · 2020
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Invertible gaussian reparameterization: Revisiting the gumbel-softmax,
A. Potapczynski, G. Loaiza-Ganem, J. P. Cunningham, · 2020
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