Transfer learning can be described as the tion of abstract knowledge from one learning domain or task and the reuse of that knowledge in a related domain or task. In categorizatio...
Kevin R. Canini, Mikhail M. Shashkov, Thomas L. Gr...
Inference in graphical models has emerged as a promising technique for planning. A recent approach to decision-theoretic planning in relational domains uses forward inference in d...
Existing approaches to analyzing the asymptotics of graph Laplacians typically assume a well-behaved kernel function with smoothness assumptions. We remove the smoothness assumpti...
The structure of a Markov network is typically learned using top-down search. At each step, the search specializes a feature by conjoining it to the variable or feature that most ...
Identifying the most influential documents in a corpus is an important problem in many fields, from information science and historiography to text summarization and news aggregati...