74 citations · 155 across the 16 of their papers we have counts for
4 papers · 1 filter
Variational Inference with Hamiltonian Monte Carlo
Christopher Wolf, Maximilian Karl, Patrick van der Smagt
Variational inference lies at the core of many state-of-the-art algorithms. To improve the approximation of the posterior beyond parametric families, it was proposed to include MCM…
Unsupervised preprocessing for Tactile Data
Maximilian Karl, Justin Bayer, Patrick van der Smagt
Tactile information is important for gripping, stable grasp, and in-hand manipulation, yet the complexity of tactile data prevents widespread use of such sensors. We make use of an…
ML-based tactile sensor calibration: A universal approach
Maximilian Karl, Artur Lohrer, Dhananjay Shah +5
We study the responses of two tactile sensors, the fingertip sensor from the iCub and the BioTac under different external stimuli. The question of interest is to which degree both…
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data
Maximilian Karl, Maximilian Soelch, Justin Bayer +1
We introduce Deep Variational Bayes Filters (DVBF), a new method for unsupervised learning and identification of latent Markovian state space models. Leveraging recent advances in…