michaelschober.net

Michael Schober

I am a PhD student at the Max-Planck-Institute for Intelligent Systems where my supervisor is Philipp Hennig. I'm working on probabilistic numerical algorithms for solving ordinary differential equations. This means that I'm curious what happens if you look at numerical algorithms from an inference/estimation perspective. More specifically, I try to answer two questions:. How can we interpret traditional numerical algorithms in an inference framework? I have written a blog post. From the MICCAI society.

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Michael Schober | michaelschober.net Reviews

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I am a PhD student at the Max-Planck-Institute for Intelligent Systems where my supervisor is Philipp Hennig. I'm working on probabilistic numerical algorithms for solving ordinary differential equations. This means that I'm curious what happens if you look at numerical algorithms from an inference/estimation perspective. More specifically, I try to answer two questions:. How can we interpret traditional numerical algorithms in an inference framework? I have written a blog post. From the MICCAI society.

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3 miscellaneous

4 michael schober

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6 on probabilistic numerics

7 in tübingen

8 new website online

9 at nips

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Michael Schober | michaelschober.net Reviews

https://michaelschober.net

I am a PhD student at the Max-Planck-Institute for Intelligent Systems where my supervisor is Philipp Hennig. I'm working on probabilistic numerical algorithms for solving ordinary differential equations. This means that I'm curious what happens if you look at numerical algorithms from an inference/estimation perspective. More specifically, I try to answer two questions:. How can we interpret traditional numerical algorithms in an inference framework? I have written a blog post. From the MICCAI society.

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michaelschober.net

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Michael Schober

http://www.michaelschober.net/misc.html

You have seen nothing yet, ba-ba-ba-baby.

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Michael Schober

http://www.michaelschober.net/publications.html

Probabilistic ODE Solvers with Runge-Kutta Means. We show that some Gaussian process ordinary differential equation solvers have the exact same mean as other well-known ODE solvers. This recovers all theoretical guarantees known for the traditional solvers while putting them in a probabilistic context. Michael Schober, David Duvenaud, Philipp Hennig:. Probabilistic ODE Solvers with Runge-Kutta Means. In: Advances of Neural Information Processing Systems (NIPS), 2014. Selected for oral presentation. Betwe...

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Michael Schober

http://www.michaelschober.net/software.html

All bleeding edge research code is written in Matlab as many of my collaborateurs use it. Let me know if you want an implementation in a specific programming language and I'll help you get started as much as possible. I have contributed to a linear programming API written in Java. SCPSolver. Is a front-end to various linear programming libraries intended for rapid prototyping and easy installation. If you want to solve small to medium-sized LPs in Java, SCPSolver might just be what you are looking for.

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I am a PhD student at the Max-Planck-Institute for Intelligent Systems where my supervisor is Philipp Hennig. I'm working on probabilistic numerical algorithms for solving ordinary differential equations. This means that I'm curious what happens if you look at numerical algorithms from an inference/estimation perspective. More specifically, I try to answer two questions:. How can we interpret traditional numerical algorithms in an inference framework? I have written a blog post. From the MICCAI society.

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