Search Results for author: Philipp Rostalski

Found 6 papers, 1 papers with code

Anatomy-guided domain adaptation for 3D in-bed human pose estimation

1 code implementation22 Nov 2022 Alexander Bigalke, Lasse Hansen, Jasper Diesel, Carlotta Hennigs, Philipp Rostalski, Mattias P. Heinrich

As a remedy, we present a novel domain adaptation method, adapting a model from a labeled source to a shifted unlabeled target domain.

3D Human Pose Estimation Anatomy +1

A real-time GP based MPC for quadcopters with unknown disturbances

no code implementations14 Oct 2022 Niklas Schmid, Jonas Gruner, Hossam S. Abbas, Philipp Rostalski

Unfortunately, the computational complexity of inference and learning on classical GPs scales cubically, which is intractable for real-time applications.

Model Predictive Control

Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions

no code implementations20 Jul 2021 Eike Petersen, Yannik Potdevin, Esfandiar Mohammadi, Stephan Zidowitz, Sabrina Breyer, Dirk Nowotka, Sandra Henn, Ludwig Pechmann, Martin Leucker, Philipp Rostalski, Christian Herzog

This survey provides an overview of the technical and procedural challenges involved in creating medical machine learning systems responsibly and in conformity with existing regulations, as well as possible solutions to address these challenges.

BIG-bench Machine Learning Federated Learning +1

Scalable Gaussian Process Regression for Kernels with a Non-Stationary Phase

no code implementations ICML 2020 Jan Graßhoff, Alexandra Jankowski, Philipp Rostalski

Our approach employs multiple sets of non-equidistant inducing points to account for the non-stationarity and retrieve Toeplitz and Kronecker structure in the kernel matrix allowing for efficient inference.

Gaussian Processes regression

On Approximate Nonlinear Gaussian Message Passing On Factor Graphs

no code implementations21 Mar 2019 Eike Petersen, Christian Hoffmann, Philipp Rostalski

Factor graphs have recently gained increasing attention as a unified framework for representing and constructing algorithms for signal processing, estimation, and control.

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