Universal Differential Equations with Gaussian Processes | Steffen Ridderbusch | SciMLCon 2022

TAMIDS Data Science Webinar: Solving Nonlinear PDEs with Gaussian Processes and Deep Neural Network

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David Duvenaud - Latent Stochastic Differential Equations: An Unexplored Model Class

CCS 2016 - Deep Learning with Differential Privacy

Robust and Stable Deep Learning Algorithms for Forward-Backward Stochastic Differential Equations

Stefano Soatto (UCLA): "Dynamics and Control of Differential Learning"

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CBL Alumni Series: Accurate Gaussian Processes and how they can help Deep Learning

Gaussian Process Learning for Power Systems

[DeepBayes2018]: Day 5, Invited talk 3. Deep Gaussian processes

Jakob Macke (UCL): Gaussian process methods for modeling neural population data

David Duvenaud (U of T) --Latent Stochastic Differential Equations

Flow Matching for Generative Modeling (Paper Explained)

Transforming Gaussian processes with normalizing flows

Paris Perdikaris - Data-driven modeling of stochastic systems using physics-aware deep learning

Maziar Raissi: "Hidden Physics Models: Machine Learning of Non-Linear Partial Differential Equat..."

SOLAR-GP: Sparse Online Locally Adaptive Regression Using Gaussian Processes for Robot Learning

Automated Timeseries Analysis with Gaussian Processes

[DeepBayes2019]: Day 4, Keynote Lecture 3. Deep Gaussian processes