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Latent SDEs on Homogeneous Spaces

Neural Information Processing Systems

We consider the problem of variational Bayesian inference in a latent variable model where a (possibly complex) observed stochastic process is governed by the solution of a latent stochastic differential equation (SDE).


Noether Embedding: Efficient Learning of Temporal Regularities Chi Gao

Neural Information Processing Systems

Learning to detect and encode temporal regularities (TRs) in events is a prerequisite for human-like intelligence. These regularities should be formed from limited event samples and stored as easily retrievable representations.