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In this work we explore a new framework for approximate Bayesian inference in large datasets based on stochastic control (i.e.
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Bayesian learning via stochastic gradient Langevin dynamics
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The data-driven Schrödinger bridge
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Bayesian model-agnostic meta-learning
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Reconciling modern machine-learning practice and the classical bias–variance trade-off
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Schrödinger bridge samplers
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Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches
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Gaussian processes for big data
Hensman, J., Fusi, N., and Lawrence, N. D. (2013) · 2013
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Stochastic variational inference
Hoffman, M. D., Blei, D. M., Wang, C., and Paisley, J. (2013) · 2013
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Data assimilation: the Schrödinger perspective
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Scalable gradients and variational inference for stochastic differential equations
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Infinitely deep Bayesian neural networks with stochastic differential equations
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Likelihood training of schrödinger bridge using forward-backward SDEs theory
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Path integral sampler: A stochastic control approach for sampling
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