A neural network model of word prediction based on automatically derived corpus-based term vectors is proposed as a replacement for the standard n-gram model. Initial testing and evaluation show the technique is promising, but more rigorous evaluation techniques are needed.

This paper presents experimental results of an original approach to the Neural Network learning architecture for the control and the adaptive control of mobile robots. The basic idea is to use non-recurrent multi-layer-network and the backpropagation algorithm without desired outputs, but with a quadratic criterion which spezify the control objective. To illustrate this method, we consider an e...

The goals of this research were to search for Convolutional Neural Network (CNN) architectures, suitable an on-device processor with limited computing resources, performing at substantially lower Architecture Search (NAS) costs. A new algorithm entitled Early Exit Population Initialisation (EE-PI) Evolutionary Algorithm (EA) was developed achieve both goals. EE-PI reduces the total number param...

There has been immense success on the application of Convolutional Neural Nets (CNN) to image and acoustic data analysis. In this paper, rather than preprocessing vibration signals to denoise or extract features, we investigate the usage of CNNs on raw signals; in particular, we test the accuracy of CNNs as classifiers on bearing fault data, by varying the configurations of the CNN from one-lay...

Deformable models are an attractive approach to recognizing objects which have considerable within-class variability such as handwritten characters. However, there are severe search problems associated with tting the models to data which could be reduced if a better starting point for the search were available. We show that by training a neural network to predict how a deformable model should b...

Kalveram, KTh., Power series and neurat-net computing, Neurocomputing 5 (1993) 165-174. A power series expansion is represented by a threeJayer feedforward network, the number of nodes in the hidden layer conesponding to the number of terms retained in the series, and the synaptic weights in the hidden layer representing the exponents used. The activation functions addrcssed to input, hidden an...

Unsupervised classiication is the classiication of data into a number of classes in such a way that data in each class are all similar to each other. In the past there have been few if any studies done to compare the performance of diierent unsupervised classiication techniques. In this paper we review Bayesian and neural net approaches to unsupervised classiication and present results of exper...

Despite having high accuracy, neural nets have been shown to be susceptible to adversarial examples, where a small perturbation to an input can cause it to become mislabeled. We propose metrics for measuring the robustness of a neural net and devise a novel algorithm for approximating these metrics based on an encoding of robustness as a linear program. We show how our metrics can be used to ev...

alzheimer disease is one form of dementia in old age. alzheimer disease, the incurable disease, which is usually in the seventh decade of human life, shows its symptoms. the disease may be present for years without clinical symptoms. the current study identified the genes with altered expression in patients with alzheimer disease. the important sequence of each gene in alzheimer disease was fou...