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We present a novel generative modeling method called diffusion normalizing flow based on stochastic differential equations (SDEs).
Reverse-time diffusion equation models
Anderson, B. D · 1982
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The mnist database of handwritten digits
LeCun, Y · 1998
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Nonlinear dimensionality reduction by locally linear embedding
Roweis, S. T. and Saul, L. K · 2000
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Sensitivity analysis using itô–malliavin calculus and martingales, and application to stochastic optimal control
Gobet, E. and Munos, R · 2005
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Smoking adjoints: Fast monte carlo greeks
Giles, M. and Glasserman, P · 2006
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Learning multiple layers of features from tiny images
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Sample complexity of testing the manifold hypothesis
Narayanan, H. and Mitter, S · 2010
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Tweedie’s formula and selection bias
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Auto-encoding variational bayes
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Made: Masked autoencoder for distribution estimation
Germain, M., Gregor, K., Murray, I., and Larochelle, H · 2015
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Variational inference with normalizing flows
Rezende, D. and Mohamed, S · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
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Density estimation using real nvp
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2016
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Wavenet: A generative model for raw audio
Oord, A. v. d., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K · 2016
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Learning to generate samples from noise through infusion training
Bordes, F., Honari, S., and Vincent, P · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
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Masked autoregressive flow for density estimation
Papamakarios, G., Pavlakou, T., and Murray, I · 2017
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Salimans, T., Karpathy, A., Chen, X., and Kingma, D. P · 2017
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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Learning gradient fields for shape generation
Cai, R., Yang, G., Averbuch-Elor, H., Hao, Z., Belongie, S., Snavely, N., and Hariharan, B · 2020
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Relaxing bijectivity constraints with continuously indexed normalising flows
Cornish, R., Caterini, A., Deligiannidis, G., and Doucet, A · 2020
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Residual energy-based models for text generation
Deng, Y., Bakhtin, A., Ott, M., Szlam, A., and Ranzato, M · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Chen, R. T., Rubanova, Y., Bettencourt, J., and Duvenaud, D · 2018
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Grathwohl, W., Chen, R. T., Bettencourt, J., Sutskever, I., and Duvenaud, D · 2018
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Transformation autoregressive networks
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Residual flows for invertible generative modeling
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A rad approach to deep mixture models
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Diffwave: A versatile diffusion model for audio synthesis
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Dynamical theories of Brownian motion
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Stochastic normalizing flows
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Improved autoregressive modeling with distribution smoothing
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