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Normalizing flows attempt to model an arbitrary probability distribution through a set of invertible mappings.
On the variance of the adaptive learning rate and beyond
Liu, L., Jiang, H., He, P., Chen, W., Liu, X., Gao, J., and Han, J. (2019) · 1908
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Use of different monte carlo sampling techniques
Kahn, H. (1955) · 1955
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014) · 1958
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A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
Hutchinson, M. F. (1990) · 1990
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Monotone linear rational spline interpolation
Fuhr, R. D. and Kallay, M. (1992) · 1992
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The MNIST database of handwritten digits
LeCun, Y. (1998) · 1998
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A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
Martin, D. R., Fowlkes, C. C., Tal, D., and Malik, J. (2001) · 2001
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Probability, random variables, and stochastic processes
Papoulis, A. and Pillai, S. U. (2002) · 2002
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ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L., Li, K., and Li, F. (2009) · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G. (2009) · 2009
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A family of nonparametric density estimation algorithms
Tabak, E. G. and Turner, C. V. (2013) · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2014) · 2014
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NICE: non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y. (2015) · 2015
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MADE: masked autoencoder for distribution estimation
Germain, M., Gregor, K., Murray, I., and Larochelle, H. (2015) · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C. (2015) · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2015) · 2015
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Variational inference with normalizing flows
Rezende, D. J. and Mohamed, S. (2015) · 2015
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Importance weighted autoencoders
Burda, Y., Grosse, R. B., and Salakhutdinov, R. (2016) · 2016
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SGDR: stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F. (2017) · 2017
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Masked autoregressive flow for density estimation
Papamakarios, G., Murray, I., and Pavlakou, T. (2017) · 2017
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Neural ordinary differential equations
Chen, R. T. Q., Rubanova, Y., Bettencourt, J., and Duvenaud, D. (2018) · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P. (2018) · 2018
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Transformation autoregressive networks
Oliva, J. B., Dubey, A., Zaheer, M., Póczos, B., Salakhutdinov, R., Xing, E. P., and Schneider, J. (2018) · 2018
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Invertible residual networks
Behrmann, J., Grathwohl, W., Chen, R. T. Q., Duvenaud, D., and Jacobsen, J. (2019) · 2019
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
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Improving variational autoencoders with inverse autoregressive flow
Kingma, D. P., Salimans, T., Józefowicz, R., Chen, X., Sutskever, I., and Welling, M. (2016) · 2016
Cited alongside, same era.
Variational lossy autoencoder
Chen, X., Kingma, D. P., Salimans, T., Duan, Y., Dhariwal, P., Schulman, J., Sutskever, I., and Abbeel, P. (2017) · 2017
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A downsampled variant of ImageNet as an alternative to the CIFAR datasets
Chrabaszcz, P., Loshchilov, I., and Hutter, F. (2017) · 2017
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EMNIST: an extension of MNIST to handwritten letters
Cohen, G., Afshar, S., Tapson, J., and van Schaik, A. (2017) · 2017
Cited alongside, same era.
Density estimation using real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S. (2017) · 2017
Cited alongside, same era.
Block neural autoregressive flow
Cao, N. D., Aziz, W., and Titov, I. (2019) · 2019
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Residual flows for invertible generative modeling
Chen, R. T. Q., Behrmann, J., Duvenaud, D., and Jacobsen, J. (2019) · 2019
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Autoregressive energy machines
Durkan, C. and Nash, C. (2019) · 2019
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FFJORD: free-form continuous dynamics for scalable reversible generative models
Grathwohl, W., Chen, R. T. Q., Bettencourt, J., Sutskever, I., and Duvenaud, D. (2019) · 2019
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Neural importance sampling
Müller, T., McWilliams, B., Rousselle, F., Gross, M., and Novák, J. (2019) · 2019
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Neural autoregressive flows
Huang, C., Krueger, D., Lacoste, A., and Courville, A. C. (2018) · 2092
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