Probabilistic movement models and zones of control

Machine Learning - Coordinated movements of players are key to success in team sports. However, traditional models for player movements are based on unrealistic assumptions and their analysis is...     

Joint optimization of an autoencoder for clustering and embedding

Machine Learning - Deep embedded clustering has become a dominating approach to unsupervised categorization of objects with deep neural networks. The optimization of the most popular methods...     

Who can receive the pass? A computational model for quantifying availability in soccer

Data Mining and Knowledge Discovery - The paper presents a computational approach to Availability of soccer players. Availability is defined as the probability that a pass reaches the target player...     

Distributed robust Gaussian Process regression

We study distributed and robust Gaussian Processes where robustness is introduced by a Gaussian Process prior on the function values combined with a Student-t likelihood. The posterior distribution is approximated by a Laplace Approximation, and together with concepts from Bayesian Committee Machines, we efficiently distribute the computations and render robust GPs on huge data sets feasible. We provide a detailed derivation and report on empirical results. Our findings on real and artificial data show that our approach outperforms existing baselines in the presence of outliers by using all available data.     

Measurement of the polarization degree of accelerated polarized electrons at the 2.5 GeV synchrotron in Bonn for energies between 0.85 and 2 GeV

The polarization degree of accelerated longitudinally polarized electrons has been measured employing the spin dependence of the elastic electron-electron cross section. We observe strong depolarization as well as polarization flips in the neighbourhood of imperfection and intrinsic resonances.

Measures to maintain the initial polarization degree up to the highest available energies are discussed.