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Diffusion models have proven to be a flexible and effective framework for modelling probability distributions on finite-dimensional spaces.
Functional variational bayesian neural networks
Sun, S., Zhang, G., Shi, J., and Grosse, R. (2019) · 1903
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Regularité des trajectoires des fonctions aléatoires gaussiennes
Fernique, X. (1975) · 1974
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Numerical Integration in the Treatment of Integral Equations
Baker, C. T. H. (1979) · 1978
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Time reversal of diffusions
Haussmann, U. G. and Pardoux, E. (1986) · 1986
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π \pi vae: Encoding stochastic process priors with variational autoencoders
Mishra, S., Flaxman, S., Berah, T., Pakkanen, M., Zhu, H., and Bhatt, S. (2020) · 2002
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Gaussian processes in machine learning
Rasmussen, C. E. (2003) · 2003
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Estimation of non-normalized statistical models by score matching
Hyvärinen, A. (2005) · 2005
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Gradient Flows in Metric Spaces and in the Space of Probability Measures
Ambrosio, L., Gigli, N., and Savaré, G. (2008) · 2008
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Eigenvalues of integral operators defined by smooth positive definite kernels
Ferreira, J. and Menegatto, V. (2009) · 2009
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Inter-domain gaussian processes for sparse inference using inducing features
Lázaro-Gredilla, M. and Figueiras-Vidal, A. (2009) · 2009
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A weak convergence approach to the theory of large deviations
Dupuis, P. and Ellis, R. S. (2011) · 2011
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A connection between score matching and denoising autoencoders
Vincent, P. (2011) · 2011
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Infinite Dimensional Analysis: A Hitchhiker’s Guide
Charalambos, D. and Aliprantis, B. (2013) · 2013
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Variational fourier features for gaussian processes
Hensman, J., Durrande, N., and Solin, A. (2018) · 2018
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Attentive neural processes
Kim, H., Mnih, A., Schwarz, J., Garnelo, M., Eslami, A., Rosenbaum, D., Vinyals, O., and Teh, Y. W. (2019) · 2019
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The functional neural process
Louizos, C., Shi, X., Schutte, K., and Welling, M. (2019) · 2019
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Sequential neural processes
Singh, G., Yoon, J., Son, Y., and Ahn, S. (2019) · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S. (2019) · 2019
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P. (2020) · 2020
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Cold diffusion: Inverting arbitrary image transforms without noise
Bansal, A., Borgnia, E., Chu, H.-M., Li, J. S., Kazemi, H., Huang, F., Goldblum, M., Geiping, J., and Goldstein, T. (2022) · 2022
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Soft diffusion: Score matching for general corruptions
Daras, G., Delbracio, M., Talebi, H., Dimakis, A. G., and Milanfar, P. (2022) · 2022
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From data to functa: Your data point is a function and you should treat it like one
Dupont, E., Kim, H., Eslami, S., Rezende, D., and Rosenbaum, D. (2022) · 2022
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Dutordoir, V., Saul, A., Ghahramani, Z., and Simpson, F. (2022) · 2022
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Yang, L., Zhang, D., and Karniadakis, G. E. (2020) · 2020
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Time reversal of diffusion processes under a finite entropy condition
Cattiaux, P., Conforti, G., Gentil, I., and Léonard, C. (2021) · 2021
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Diffusion models beat GAN on image synthesis
Dhariwal, P. and Nichol, A. (2021) · 2021
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Skilful precipitation nowcasting using deep generative models of radar
Ravuri, S., Lenc, K., Willson, M., Kangin, D., Lam, R., Mirowski, P., Fitzsimons, M., Athanassiadou, M., Kashem, S., Madge, S., Prudden, R., Mandhane, A., Clark, A., Brock, A., Simonyan, K., Hadsell, R., Robinson, N., Clancy, E., Arribas, A., and Mohamed, S. (2021) · 2021
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Image super-resolution via iterative refinement
Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D. J., and Norouzi, M. (2021) · 2021
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B. (2021) · 2021
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Guth, F., Coste, S., De Bortoli, V., and Mallat, S. (2022) · 2022
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Cascaded diffusion models for high fidelity image generation
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Hoogeboom, E. and Salimans, T. (2022) · 2022
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The neural process family: Survey, applications and perspectives
Jha, S., Gong, D., Wang, X., Turner, R. E., and Yao, L. (2022) · 2022
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Subspace diffusion generative models
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ℱ \mathcal{F} -ebm: Energy based learning of functional data
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Generative Modelling With Inverse Heat Dissipation
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A kernel two-sample test for functional data
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