no code implementations • 2 Aug 2019 • Ozan İrsoy, Rakesh Gosangi, Haimin Zhang, Mu-Hsin Wei, Peter Lund, Duccio Pappadopulo, Brendan Fahy, Neophytos Nephytou, Camilo Ortiz
In this paper, we introduce a new model architecture, directed-acyclic-graph LSTM (DAG-LSTM) for DA classification.
no code implementations • 28 Dec 2015 • Khalifeh AlJadda, Mohammed Korayem, Camilo Ortiz, Trey Grainger, John A. Miller, Khaled Rasheed, Krys J. Kochut, William S. York, Rene Ranzinger, Melody Porterfield
In this paper we introduce an extension to Bayesian Networks to handle massive sets of hierarchical data in a reasonable amount of time and space.
no code implementations • 21 Jul 2014 • Khalifeh AlJadda, Mohammed Korayem, Camilo Ortiz, Trey Grainger, John A. Miller, William S. York
When modeling this kind of hierarchical data across large data sets, Bayesian networks become infeasible for representing the probability distributions for the following reasons: i) Each level represents a single random variable with hundreds of thousands of values, ii) The number of levels is usually small, so there are also few random variables, and iii) The structure of the network is predefined since the dependency is modeled top-down from each parent to each of its child nodes, so the network would contain a single linear path for the random variables from each parent to each child node.