Recently proposed l1-regularized maximum-likelihood optimization methods for learning sparse Markov networks result into convex problems that can be solved optimally and efficien...
Building on the work of Martinetz, Schulten and de Silva, Carlsson, we introduce a 2-parameter family of witness complexes and algorithms for constructing them. This family can be ...
Dominique Attali, Herbert Edelsbrunner, John Harer...
The ability to understand the factors contributing to parallel program performance are vital for understanding the impact of machine parameters on the performance of specific app...
Matthew J. Sottile, Vaddadi P. Chandu, David A. Ba...
: Markov models have been proposed for the classification of DNA-motifs using generative approaches for parameter learning. Here, we propose to apply the discriminative paradigm fo...
Jan Grau, Jens Keilwagen, Alexander E. Kel, Ivo Gr...
Abstract. Isomap is a highly popular manifold learning and dimensionality reduction technique that effectively performs multidimensional scaling on estimates of geodesic distances....