Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is usin...
Approximate MAP inference in graphical models is an important and challenging problem for many domains including computer vision, computational biology and natural language unders...
We present a novel approach to constraintbased causal discovery, that takes the form of straightforward logical inference, applied to a list of simple, logical statements about ca...
Developing intuitive and efficient methods for shape editing is one of the most important areas in computer graphics, and free-form deformation (FFD), which is one of such methods...
Background: Knowledge-based potentials have been widely used in the last 20 years for fold recognition, protein structure prediction from amino acid sequence, ligand binding, prot...
Yaping Feng, Andrzej Kloczkowski, Robert L. Jernig...