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Wasserstein Neural Processes
Neural Processes (NPs) are a class of models that learn a mapping from a context set of input-output pairs to a distribution over functions.
Video Extrapolation with an Invertible Linear Embedding
We predict future video frames from complex dynamic scenes, using an invertible neural network as the encoder of a nonlinear dynamic system with latent linear state evolution.
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