Statistical topic models provide a general data-driven framework for automated discovery of high-level knowledge from large collections of text documents. While topic models can p...
Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyv...
This paper investigates adapting a lexicalized probabilistic context-free grammar (PCFG) to a novel domain, using maximum a posteriori (MAP) estimation. The MAP framework is gener...
Using traditional semantic data modeling, multi-level modeling can be achieved by representing objects in different abstraction hierarchies, namely classification, aggregation and...
This paper describes a comprehensive approach to construct quality meshes for implicit solvation models of biomolecular structures starting from atomic resolution data in the Prot...
: Combining multiple data sources, each with its own features, to achieve optimal inference has received a lot of attention in recent years. In inference from multiple data sources...
Shankara B. Subramanya, Zheshen Wang, Baoxin Li, H...