In this paper a genetic algorithm (GA) approach to design of multi-layer perceptron (MLP) for
combined cycle power plant power output estimation is presented. Dataset used in this …

We propose a new variational inference method based on the Kullback-Leibler (KL)
proximal term. We make two contributions towards improving efficiency of variational …

We propose an iterative nonlinear estimator based on the technique of variational Bayesian
optimization. The posterior distribution of the underlying system state is approximated by a …

Deep learning models have been shown to have great advantages in answer selection
tasks. The existing models, which employ encoder-decoder recurrent neural network (RNN) …

In this paper we present an approach for scaling up Bayesian learning using variational
methods by exploiting distributed computing clusters managed by modern big data …

Motivated by a real-world financial dataset, we propose a distributed variational message
passing scheme for learning conjugate exponential models. We show that the method can …

Distributed Stochastic Gradient optimization algorithms are studied extensively to address
challenges in centralized approaches, such as data privacy, communication load, and …

Variational Bayesian methods have met a huge success over the last years, and remain a
very active research topic. However, little is known on the convergence properties of the …

In this thesis, we discuss and develop randomized algorithms for big data problems. In
particular, we study the finite-sum optimization with newly emerged variance-reduction …