The problems of dimension reduction and inference of statistical dependence are addressed by the modeling framework of learning gradients. The models we propose hold for Euclidean...
Abstract. Computational inference of transcriptional regulatory networks remains a challenging problem, in part due to the lack of strong network models. In this paper we present e...
Although the ability to model and infer Attacker Intent, Objectives and Strategies (AIOS) may dramatically advance the literature of risk assessment, harm prediction, and predicti...
In this paper we discuss why a simple network topology inference algorithm based on network co-occurrence measurements and a Markov random walk model for routing enables perfect t...
Human categorization research is dominated by work in classification learning. The field may be in danger of equating the classification learning paradigm with the more general ph...
Bradley C. Love, Arthur B. Markman, Takashi Yamauc...