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The widespread availability of satellite images has allowed researchers to model complex systems such as disease dynamics.
Navier-stokes, fluid dynamics, and image and video inpainting
Bertalmio, M., Bertozzi, A. L., and Sapiro, G · 2001
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Fast digital image inpainting
Richard, M. and Chang, M. Y.-S · 2001
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Multiscale structural similarity for image quality assessment
Wang, Z., Simoncelli, E. P., and Bovik, A. C · 2003
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An image inpainting technique based on the fast marching method
Telea, A · 2004
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Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P · 2004
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Landsat 7 scan line corrector-off gap-filled product development
Scaramuzza, P. and Barsi, J · 2005
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Cahn–hilliard inpainting and a generalization for grayvalue images
Burger, M., He, L., and Schönlieb, C.-B · 2009
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Unconditionally stable schemes for higher order inpainting
Bertozzi, A. and Schönlieb, C.-B · 2011
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Learning to learn
Thrun, S. and Pratt, L · 2012
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
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Context encoders: Feature learning by inpainting
Pathak, D., Krähenbühl, P., Donahue, J., Darrell, T., and Efros, A. A · 2016
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High-resolution image inpainting using multi-scale neural patch synthesis
Yang, C., Lu, X., Lin, Z., Shechtman, E., Wang, O., and Li, H · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
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Google earth engine: Planetary-scale geospatial analysis for everyone
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R · 2017
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Globally and locally consistent image completion
Iizuka, S., Simo-Serra, E., and Ishikawa, H · 2017
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Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R., and Smola, A · 2017
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Patch-based image inpainting with generative adversarial networks
Demir, U. and Ünal, G. B · 2018
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Neural process family
Dubois, Y., Gordon, J., and Foong, A. Y · 2020
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Meta-learning stationary stochastic process prediction with convolutional neural processes
Foong, A., Bruinsma, W., Gordon, J., Dubois, Y., Requeima, J., and Turner, R · 2020
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Convolutional conditional neural processes
Gordon, J., Bruinsma, W. P., Foong, A. Y., Requeima, J., Dubois, Y., and Turner, R. E · 2020
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Handling incomplete heterogeneous data using vaes
Nazabal, A., Olmos, P. M., Ghahramani, Z., and Valera, I · 2020
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Using publicly available satellite imagery and deep learning to understand economic well-being in africa
Yeh, C., Perez, A., Driscoll, A., Azzari, G., Tang, Z., Lobell, D., Ermon, S., and Burke, M · 2020
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Generative models as distributions of functions
Dupont, E., Teh, Y. W., and Doucet, A · 2021
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Lee, J., Lee, Y., Kim, J., Kosiorek, A. R., Choi, S., and Teh, Y. W · 2018
Cited alongside, same era.
Image inpainting for irregular holes using partial convolutions
Liu, G., Reda, F. A., Shih, K. J., Wang, T., Tao, A., and Catanzaro, B · 2018
Cited alongside, same era.
A qualitative study of exemplar based image inpainting
Shroff, M. and Bombaywala, M. S. R · 2019
Cited alongside, same era.
Sequential neural processes
Singh, G., Yoon, J., Son, Y., and Ahn, S · 2019
Cited alongside, same era.
Mapping the global prevalence, incidence, and mortality of plasmodium falciparum, 2000–17: a spatial and temporal modelling study
Weiss, D. J., Lucas, T. C., Nguyen, M., Nandi, A. K., Bisanzio, D., Battle, K. E., Cameron, E., Twohig, K. A., Pfeffer, D. A., Rozier, J. A., et al · 2019
Cited alongside, same era.
The gaussian neural process
Bruinsma, W. P., Requeima, J., Foong, A. Y., Gordon, J., and Turner, R. E · 2020
Cited alongside, same era.
Conditional neural processes
Garnelo, M., Rosenbaum, D., Maddison, C., Ramalho, T., Saxton, D., Shanahan, M., Teh, Y. W., Rezende, D., and Eslami, S. A
Cited in the paper.
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Efficient gaussian neural processes for regression
Markou, S., Requeima, J., Bruinsma, W., and Turner, R · 2021
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Aggregated gaussian processes with multiresolution earth observation covariates, 2021
Zhu, H., Howes, A., van Eer, O., Rischard, M., Li, Y., Sejdinovic, D., and Flaxman, S · 2021
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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. M. A., Rezende, D. J., and Rosenbaum, D · 2022
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Repaint: Inpainting using denoising diffusion probabilistic models
Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., and Van Gool, L · 2022
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Practical conditional neural processes via tractable dependent predictions
Markou, S., Requeima, J., Bruinsma, W. P., Vaughan, A., and Turner, R. E · 2022
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