Uncovering the mechanisms of physics is driving a new paradigm in artificial intelligence
(AI) discovery. Today, physics has enabled us to understand the AI paradigm in a wide …

Recently, the explosive increase in social media data enables manufacturers to collect
product defect information promptly. Extant literature gathers defect information like defective …

Sum-product networks (SPNs) are a recently proposed type of probabilistic graphical
models allowing complex variable interactions while still granting efficient inference. In this …

Reinforcement Learning (RL) usually needs thousands of episodes, leading its applications
on physical robots expensive and challenging. Little research has been reported about …

Sum-product networks (SPNs) are a promising and novel type of probabilistic model, which
has been receiving significant attention in recent years. There are, however, several open …

Tree augmented naïve Bayes classifier (TAN) has been widely used in machine learning
and data mining. To improve the flexibility and classification performance of TAN, this paper …

In this paper, we present the implementation of two types of Bayesian inference problems to
demonstrate the potential of building probabilistic algorithms in hardware using single set of …

Bayesian network classifier (BNCs) are typically implemented on nowadays desktop
computers. However, many real world applications require classifier implementation on …

There is an emerging industrial demand for predictive maintenance algorithms that exhibit
high levels of predictive accuracy. Such condition monitoring tools must estimate dynamic …

This paper introduces integer Bayesian network classifiers (BNCs), ie BNCs with discrete
valued nodes where parameters are stored as integer numbers. These networks allow for …