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We propose a new Bayesian Neural Net formulation that affords variational inference for which the evidence lower bound is analytically tractable subject to a tight approximation.
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Rectifier nonlinearities improve neural network acoustic models
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Fast dropout training
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Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2015
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Weight uncertainty in neural networks
C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wiestra · 2015
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Bayesian convolutional neural networks with bernoulli approximate variational inference
Y. Gal and Z. Ghahramani · 2015
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Explaining and harnessing adversarial examples
I.J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Stochastic gradient Hamiltonian monte carlo
T. Chen, E.B. Fox, and C. Guestrin · 2017
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Variational Gaussian dropout is not Bayesian
J. Hron, A.G. Matthews, and Z.Ghahramani · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger · 2017
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What uncertainties do we need in Bayesian deep learning for computer vision?
A. Kendall and Y. Gal · 2017
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Dropout inference in Bayesian neural networks with alpha-divergences
Y. Li and Y. Gal · 2017
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Multiplicative normalizing flows for variational Bayesian neural networks
C. Louizos and M. Welling · 2017
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Probabilistic backpropagation for scalable learning of bayesian neural networks
J. M. Hernández-Lobato and R. Adams · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Variational dropout and the local reparameterization trick
D. Kingma, T. Salimans, and M. Welling · 2015
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Adam: A method for stochastic optimization
D.P. Kingma and J. Ba · 2015
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Variational inference with normalizing flows
D.J. Rezende and S. Mohamed · 2015
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Striving for simplicity: The all convolutional net
J.T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2015
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Bayesian compression for deep learning
C. Louizos, K. Ullrich, and M. Welling · 2017
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Reducing reparameterization gradient variance
A. Miller, N. Foti, A. D’Amour, and R.P. Adams · 2017
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Variational dropout sparsifies deep neural networks
D. Molchanov, A. Ashukha, and D. Vetrov · 2017
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Structured Bayesian pruning via log-normal multiplicative noise
K. Neklyudov, D. Molchanov, A. Ashuka, and D. Vetrov · 2017
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Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
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Sticking the landing: Simple, lower-variance gradient estimators for variational inference
G. Roeder, Y. Wu, and D. Duvenaud · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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F. Yu, V. Koltun, and T. Funkhouser · 2017
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Variational closed-form deep neural net inference
M. Kandemir · 2018
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Adaptive network sparsification via dependent variational beta-bernoulli dropout
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Yolov3: An incremental improvement
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Deterministic variational inference for robust bayesian neural networks
A. Wu, S. Nowozin, E. Meeds, R. E. Turner, J. M. Hernandez-Lobato, and A. L. Gaunt · 2019
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