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Discrete-time diffusion-based generative models and score matching methods have shown promising results in modeling high-dimensional image data.
Approximate integration of stochastic differential equations
Milshtein, G · 1975
Earlier work this paper cites.
Reverse-time diffusion equation models
Anderson, B. D · 1982
Earlier work this paper cites.
Reverse time diffusions
Elliott, R. J. and Anderson, B. D · 1985
Earlier work this paper cites.
An entropy approach to the time reversal of diffusion processes
Föllmer, H · 1985
Earlier work this paper cites.
Time reversal of diffusions
Haussmann, U. G. and Pardoux, E · 1986
Earlier work this paper cites.
A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
Hutchinson, M. F · 1989
Earlier work this paper cites.
Python reference manual
Van Rossum, G. and Drake Jr, F. L · 1995
Earlier work this paper cites.
Equilibrium free-energy differences from nonequilibrium measurements: A master-equation approach
Jarzynski, C · 1997
Earlier work this paper cites.
A variational representation for certain functionals of brownian motion
Boué, M., Dupuis, P., et al · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Monotonic networks
Sill, J · 1998
Earlier work this paper cites.
Elements of information theory
Cover, T. M · 1999
Earlier work this paper cites.
Estimating monotonic functions and their bounds
Kay, H. and Ungar, L. H · 2000
Earlier work this paper cites.
Annealed importance sampling
Neal, R. M · 2001
Earlier work this paper cites.
Stochastic differential equations
Øksendal, B · 2003
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Hyvärinen, A. and Dayan, P · 2005
Earlier work this paper cites.
A guide to NumPy , volume 1
Oliphant, T. E · 2006
Earlier work this paper cites.
Matplotlib: A 2d graphics environment
Hunter, J. D · 2007
Earlier work this paper cites.
Python for scientific computing
Oliphant, T. E · 2007
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Interpretation and generalization of score matching
Lyu, S · 2009
Cited alongside, same era.
Monotone and partially monotone neural networks
Daniels, H. and Velikova, M · 2010
Cited alongside, same era.
The numpy array: a structure for efficient numerical computation
Van Der Walt, S., Colbert, S. C., and Varoquaux, G · 2011
Cited alongside, same era.
A connection between score matching and denoising autoencoders
Vincent, P · 2011
Cited alongside, same era.
The numpy array: a structure for efficient numerical computation
Walt, S. v. d., Colbert, S. C., and Varoquaux, G · 2011
Cited alongside, same era.
3d shape generation and completion through point-voxel diffusion
Zhou, L., Du, Y., and Wu, J · 2011
Cited alongside, same era.
A rad approach to deep mixture models
Dinh, L., Sohl-Dickstein, J., Larochelle, H., and Pascanu, R · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
Later among the works it cites.
Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
Later among the works it cites.
Neural stochastic differential equations: Deep latent gaussian models in the diffusion limit
Tzen, B. and Raginsky, M · 2019
Later among the works it cites.
Learning gradient fields for shape generation
Cai, R., Yang, G., Averbuch-Elor, H., Hao, Z., Belongie, S., Snavely, N., and Hariharan, B · 2020
Later among the works it cites.
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{ \{ SciPy } \} : Open source scientific tools for { \{ Python } \}
Jones, E., Oliphant, T., and Peterson, P · 2014
Cited alongside, same era.
Brownian motion and stochastic calculus , volume 113
Karatzas, I. and Shreve, S · 2014
Cited alongside, same era.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
Cited alongside, same era.
Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
Cited alongside, same era.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
Cited alongside, same era.
Array programming with numpy
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., et al · 2020
Later among the works it cites.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Later among the works it cites.
Diffwave: A versatile diffusion model for audio synthesis
Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B · 2020
Later among the works it cites.
Scalable gradients for stochastic differential equations
Li, X., Wong, T.-K. L., Chen, R. T., and Duvenaud, D · 2020
Later among the works it cites.
Survae flows: Surjections to bridge the gap between vaes and flows
Nielsen, D., Jaini, P., Hoogeboom, E., Winther, O., and Welling, M · 2020
Later among the works it cites.
Improved techniques for training score-based generative models
Song, Y. and Ermon, S · 2020
Later among the works it cites.
Sliced score matching: A scalable approach to density and score estimation
Song, Y., Garg, S., Shi, J., and Ermon, S · 2020
Later among the works it cites.
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