: This paper reports on preliminary results of an explorative study of a protocol analysis of team learning while designing using in-situ data. Two measurement-based frameworks are...
In this paper we investigate algorithms and lower bounds for summarization problems over a single pass data stream. In particular we focus on histogram construction and K-center c...
We present a sparse approximation approach for dependent output Gaussian processes (GP). Employing a latent function framework, we apply the convolution process formalism to estab...
We show how improved sequences for magnetic resonance imaging can be found through optimization of Bayesian design scores. Combining approximate Bayesian inference and natural ima...
Matthias W. Seeger, Hannes Nickisch, Rolf Pohmann,...
Non-negative tensor factorization (NTF) is a relatively new technique that has been successfully used to extract significant characteristics from polyadic data, such as data in s...