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Irregularly-sampled time series occur in many domains including healthcare.
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A recurrent latent variable model for sequential data
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Deep learning face attributes in the wild
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MIMIC-III, a freely accessible critical care database
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Improved variational inference with inverse autoregressive flow
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Adversarial feature learning
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Adversarially learned inference
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GANs trained by a two time-scale update rule converge to a local nash equilibrium
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Deep sets
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Recurrent neural networks for multivariate time series with missing values
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Semi-implicit variational inference
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Estimating missing data in temporal data streams using multi-directional recurrent neural networks
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Large scale adversarial representation learning
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Attentive neural processes
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Set transformer: A framework for attention-based permutation-invariant neural networks
Lee, J., Lee, Y., Kim, J., Kosiorek, A., Choi, S., and Teh, Y. W · 2019
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MisGAN: Learning from incomplete data with generative adversarial networks
Li, S. C.-X., Jiang, B., and Marlin, B · 2019
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Conditional neural processes
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Garnelo, M., Schwarz, J., Rosenbaum, D., Viola, F., Rezende, D. J., Eslami, S., and Teh, Y. W
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EDDI: Efficient dynamic discovery of high-value information with partial VAE
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MIWAE: Deep generative modelling and imputation of incomplete data sets
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Latent ordinary differential equations for irregularly-sampled time series
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