Klaus-Robert Müller

TL;DR: This paper presents a new compression technique for 3D dynamic meshes, referred to as FAMC - Frame-based Animated Mesh Compression, recently promoted within the MPEG-4 standard as Amen-dement 2 of part 16 (AFX -Animation Framework extension).

TL;DR: It is shown that learning in a discriminative and stationary subspace is advantageous for BCI application and outperforms the standard SSA method.

TL;DR: It is shown that Long-Range Temporal Correlations (LRTCs) estimated from the amplitude of EEG oscillations over a range of time-scales predict performance in a complex sensorimotor task, based on Brain-Computer Interfacing (BCI).

TL;DR: This work proposes a 2-layer training scheme that enables GDML to learn an effective coarse-grained (CG) model from all-atom simulation data in a sample efficient manner and yields a smaller free energy error than neural networks when the training set is small, and a comparably high accuracy when theTraining set is sufficiently large.

TL;DR: In this paper, the authors compare four popular neural network architectures, studies the effect of pretraining, evaluates the robustness of the considered alignment preprocessings via cross-method test set swapping and intuitively visualizes the model's prediction strategies in given preprocessing conditions using the recent Layer-wise Relevance Propagation (LRP) algorithm.