Harri Lähdesmäki
3 papers in the PaperMetrix corpus
Papers by this author
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LEARNING STOCHASTIC DIFFERENTIAL EQUATIONS WITH GAUSSIAN PROCESSES WITHOUT GRADIENT MATCHING
2018
We introduce a novel paradigm for learning non-parametric drift and diffusion functions for stochastic differential equation (SDE). The proposed model learns to simulate path distributions that match observations with non-uniform time increments and arbitrary sparseness, …
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Deep learning with differential Gaussian process flows
2018 · Research Explorer (The University of Manchester)
We propose a novel deep learning paradigm of differential flows that learn a stochastic differential equation transformations of inputs prior to a standard classification or regression function. The key property of differential Gaussian processes is …
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Variational multiple shooting for Bayesian ODEs with Gaussian processes
2021 · arXiv (Cornell University)
Recent machine learning advances have proposed black-box estimation of unknown continuous-time system dynamics directly from data. However, earlier works are based on approximative ODE solutions or point estimates. We propose a novel Bayesian nonparametric model …