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Found 6 papers, 6 papers with code
Amortized Reparametrization: Efficient and Scalable Variational Inference for Latent SDEs
We consider the problem of inferring latent stochastic differential equations (SDEs) with a time and memory cost that scales independently with the amount of data, the total length of the time series, and the stiffness of the approximate differential equations.
Weak Form Generalized Hamiltonian Learning
We present a method for learning generalized Hamiltonian decompositions of ordinary differential equations given a set of noisy time series measurements.
Quadruply Stochastic Gaussian Processes
We introduce a stochastic variational inference procedure for training scalable Gaussian process (GP) models whose per-iteration complexity is independent of both the number of training points, $n$, and the number basis functions used in the kernel approximation, $m$.
Discretely Relaxing Continuous Variables for tractable Variational Inference
We explore a new research direction in Bayesian variational inference with discrete latent variable priors where we exploit Kronecker matrix algebra for efficient and exact computations of the evidence lower bound (ELBO).
Exploiting Structure for Fast Kernel Learning
We propose two methods for exact Gaussian process (GP) inference and learning on massive image, video, spatial-temporal, or multi-output datasets with missing values (or "gaps") in the observed responses.
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)
We introduce a kernel approximation strategy that enables computation of the Gaussian process log marginal likelihood and all hyperparameter derivatives in $\mathcal{O}(p)$ time.
