This paper describes, and discusses Bayesian Neural Network (BNN). The paper showcases a few different applications of them for classification and regression problems. BNNs are comprised of a Probabilistic Model and a Neural Network. The intent of such a design is to combine the strengths of Neural Networks and Stochastic modeling. Neural Networks exhibit universal continuous function approxima...

This paper provides guidance to some of the concepts surrounding recurrent neural networks. Contrary to feedforward networks, recurrent networks can be sensitive, and be adapted to past inputs. Backpropagation learning is described for feedforward networks, adapted to suit our (probabilistic) modeling needs, and extended to cover recurrent networks. The aim of this brief paper is to set the sce...

A key contributing factor to incredible success of deep neural networks has been the significant rise on massively parallel computing devices allowing researchers to greatly increase the size and depth of deep neural networks, leading to significant improvements in modeling accuracy. Although deeper, larger, or complex deep neural networks have shown considerable promise, the computational comp...

Electroencephalogram (EEG) signal is a modest measure of electric flow in a human brain. It is responsible for information flow through the neurons in the brain which controls and monitors the full torso. Hence, to widening and in-depth understanding of effectiveness in EEG signal analysis is the primary focus of this paper. Moreover, machine learning techniques often proven as more efficacious...

At the end of the 19 th century, Camillo Golgi discovered that neurons were enwrapped by a net, but it was only recently that neuroscientists started to intensely investigate the role of this mysterious reticular structure. Today, perineuronal nets (PNNs) are known to be lattice-like extracellular matrix aggregates surrounding synapses at the soma, proximal axons and dendrites of several types ...

This paper discusses the global output convergence for continuous time recurrent neural networks with continuous decreasing as well as increasing activation functions in probabilistic metric space. We establish three sufficient conditions to guarantee the global output convergence of this class of neural networks. The present result does not require symmetry in the connection weight matrix. The...

Belief networks (or probabilistic networks) and neural networks are two forms of network representations that have been used in the development of intelligent systems in the eld of arti cial intelligence. Belief networks provide a concise representation of general probability distributions over a set of random variables, and facilitate exact calculation of the impact of evidence on propositions...

The analysis on the online finger gesture recognition using multi-channel sEMG signals was explored in this paper. Nine types of gestures were applied to be identified, involving six kinds of numerical finger gestures and three kinds of hand gestures. The time domain parameters were extracted to be the features. And then, the probabilistic neural network was utilized to classify the proposed ge...

Investment theory has as a major tenet the concept of efficient markets. In an efficient market all information is fully reflected in the price of a stock. As such, there is no trading strategy based on known data that can earn an abnormal profit. Using price and volume data, this design and subsequent model produces a weak form test of the efficient markets hypothesis. First, an Object-Oriente...

We introduce a method for using deep neural networks to amortize the cost of inference in models from the family induced by universal probabilistic programming languages, establishing a framework that combines the strengths of probabilistic programming and deep learning methods. We call what we do “compilation of inference” because our method transforms a denotational specification of an infere...