Continual learning models allow them to learn and adapt new changes tasks over time. However, in continual sequential scenarios, which the are trained using different data with various distributions, neural networks (NNs) tend forget previously learned knowledge. This phenomenon is often referred as catastrophic forgetting. The forgetting an inevitable problem for dynamic environments. To addre...

Probabilistic graphical models such as Bayesian networks are widely used to model stochastic systems perform various types of analysis probabilistic prediction, risk analysis, and system health monitoring, which can become computationally expensive in large-scale systems. While demonstrations true quantum supremacy remain rare, computing applications managing exploit the advantages amplitude am...

The process of building a Bayesian network may occur in stages, in which intermediate Bayesian networks are built during preliminary processing and then used in the construction of further Bayesian networks. For example, in (Doshi, Greenwald, & Clarke 2001) we describe a way to use Bayesian networks to model and correct errors in noisy datasets. The corrected datasets are then used in (Doshi 20...