stochastic process
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High Rank Path Development: an approach to learning the filtration of stochastic processes
To address this shortcoming of weak convergence, D. Aldous [
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Latent SDEs on Homogeneous Spaces
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).
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Bayesian Learning via Q-Exponential Process
Regularization is one of the most fundamental topics in optimization, statistics and machine learning.
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Supplementary to: Geometric Neural Diffusion Processes A Organisation of appendices
B.5 Langevin dynamics Under mild assumptions on r log p
Geometric Neural Diffusion Processes Emile Mathieu University Of Cambridge Vincent Dutordoir
Traditional denoising diffusion models are defined on finite-dimension Euclidean spaces (Song and Ermon, 2019; Song et al., 2021; Ho et al., 2020; Dhariwal and Nichol, 2021).
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