Much recent research has been devoted to learning algorithms for deep architectures such as Deep Belief Networks and stacks of auto-encoder variants, with impressive results obtai...
Dumitru Erhan, Yoshua Bengio, Aaron C. Courville, ...
We present two novel perturbation-based linkage learning algorithms that extend LINC [5]; a version of LINC optimised for decomposition tasks (oLINC) and a hierarchical version of...
We provide an analytical comparison between discounted and average reward temporal-difference (TD) learning with linearly parameterized approximations. We first consider the asympt...
Abstract. This paper presents the extension of an existing mimimally supervised rule acquisition method for relation extraction by coreference resolution (CR). To this end, a novel...
We present an online learning algorithm for training parsers which allows for the inclusion of multiple objective functions. The primary example is the extension of a standard sup...
Keith Hall, Ryan T. McDonald, Jason Katz-Brown, Mi...