In the age of artificial intelligence, the best approach to handling huge amounts of data is a
tremendously motivating and hard problem. Among machine learning models, stochastic …

Numerous intriguing optimization problems arise as a result of the advancement of machine
learning. The stochastic first-order method is the predominant choice for those problems due …

This paper focuses on the joint estimation of parameters and topologies of multivariate
graphical autoregressive moving-average (ARMA) processes. Since the graphical structure …

Gradient-based algorithms are commonly used for training radial basis function neural
network (RBFNN). However, it is still difficult to avoid vanishing gradient to improve the …

Shale oil reserves are abundant worldwide and are a primary focus for future oil and gas
development. Shale reservoirs are dense, highly heterogeneous, and have a low oil …

Abstract Stochastic Gradient Descent (SGD) is a highly efficient optimization algorithm,
particularly well suited for large datasets due to its incremental parameter updates. In this …

In this work, we investigate stochastic quasi-Newton methods for minimizing a finite sum of
cost functions over a decentralized network. We first develop a general algorithmic …

Integral imaging (InIm) is useful for passive ranging and 3D visualization of partially-
occluded objects. We consider 3D object localization within a scene and in occlusions. 2D …

Data-driven evolutionary algorithms (DDEAs) have received increasing attention during the
past decade, but most existing studies are dedicated to solving relatively small-scale …

The main objective and contribution of this paper was/is the application of our knowledge-
based data-mining approach (a fuzzy rule-based classification system) characterized by a …