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A normalizing flow models a complex probability density as an invertible transformation of a simple base density.
Piecewise rational quadratic interpolation to monotonic data
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Dropout: A simple way to prevent neural networks from overfitting
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NICE: Non-linear independent components estimation
L. Dinh, D. Krueger, and Y. Bengio · 2015
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MADE: Masked autoencoder for distribution estimation
M. Germain, K. Gregor, I. Murray, and H. Larochelle · 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 inference with normalizing flows
D. J. Rezende and S. Mohamed · 2015
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ImageNet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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Importance weighted autoencoders
Y. Burda, R. B. Grosse, and R. Salakhutdinov · 2016
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Improved variational inference with inverse autoregressive flow
D. P. Kingma, T. Salimans, R. Jozefowicz, X. Chen, I. Sutskever, and M. Welling · 2016
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Improving variational auto-encoders using Householder flow
J. M. Tomczak and M. Welling · 2016
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Glow: Generative flow with invertible
D. P. Kingma and P. Dhariwal · 2018
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Reversible recurrent neural networks
M. MacKay, P. Vicol, J. Ba, and R. B. Grosse · 2018
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T. Müller, B. McWilliams, F. Rousselle, M. Gross, and J. Novák · 2018
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Transformation autoregressive networks
J. B. Oliva, A. Dubey, M. Zaheer, B. Póczos, R. Salakhutdinov, E. P. Xing, and J. Schneider · 2018
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Preprocessed datasets for MAF experiments, 2018
G. Papamakarios · 2018
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WaveGlow: A flow-based generative network for speech synthesis
R. Prenger, R. Valle, and B. Catanzaro · 2018
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D. Dheeru and E. Karra Taniskidou · 2017
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J. V. Dillon, I. Langmore, D. Tran, E. Brevdo, S. Vasudevan, D. Moore, B. Patton, A. Alemi, M. Hoffman, and R. A. Saurous · 2017
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Density estimation using Real NVP
L. Dinh, J. Sohl-Dickstein, and S. Bengio · 2017
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The reversible residual network: Backpropagation without storing activations
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Maximum entropy flow networks
G. Loaiza-Ganem, Y. Gao, and J. P. Cunningham · 2017
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SGDR: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2017
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D. J. Rezende and F. Viola · 2018
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Deep diffeomorphic normalizing flows
H. Salman, P. Yadollahpour, T. Fletcher, and K. Batmanghelich · 2018
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Sylvester normalizing flows for variational inference
R. van den Berg, L. Hasenclever, J. M. Tomczak, and M. Welling · 2018
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Block neural autoregressive flow
N. De Cao, I. Titov, and W. Aziz · 2019
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Cubic-spline flows
C. Durkan, A. Bekasov, I. Murray, and G. Papamakarios · 2019
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Flow++: Improving flow-based generative models with variational dequantization and architecture design
J. Ho, X. Chen, A. Srinivas, Y. Duan, and P. Abbeel · 2019
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Emerging convolutions for generative normalizing flows
E. Hoogeboom, R. van den Berg, and M. Welling · 2019
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Sum-of-squares polynomial flow
P. Jaini, K. A. Selby, and Y. Yu · 2019
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VideoFlow: A flow-based generative model for video
M. Kumar, M. Babaeizadeh, D. Erhan, C. Finn, S. Levine, L. Dinh, and D. Kingma · 2019
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Autoregressive energy machines
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Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows
G. Papamakarios, D. C. Sterratt, and I. Murray · 2019
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Latent normalizing flows for discrete sequences
Z. M. Ziegler and A. M. Rush · 2019
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