Effective learning in multi-label classification (MLC) requires an ate level of abstraction for representing the relationship between each instance and multiple categories. Curren...
We present black-box techniques for learning how to interleave the execution of multiple heuristics in order to improve average-case performance. In our model, a user is given a s...
Matthew J. Streeter, Daniel Golovin, Stephen F. Sm...
We present a CNF to Tree-of-BDDs (ToB) compiler with complexity at most exponential in the tree width. We then present algorithms for interesting queries on ToB. Although some of ...
Sathiamoorthy Subbarayan, Lucas Bordeaux, Youssef ...
Abstract. We propose a generic framework and methods for simplification of large networks. The methods can be used to improve the understandability of a given network, to complemen...
We present a novel method for the discovery and statistical representation of motion patterns in a scene observed by a static camera. Related methods involving learning of pattern...