Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that has gain increasing attention over the last few years. While several methods have been proposed to explain network predictions, only a few attempts to analyze them from a theoretical perspective have been made in the past. In this work, we analyze various state-of-the-art attribution methods and p...

x R el u (x ) ReLU function and its derivative ReLU(x) ReLU’(x) h (0) 0 Bias h (0) 1 Input #2 h (0) 2 Input #3 h (0) 3 Input #4 h (0) 4 Input #5 h (0) 5 Input #6 h (1) 0 h (1) 1 h (1) 2 h (1) 3 h (1) 4 h (1) 5 h (1) 6 h (h) 0 h (h) 1 h (h) 2 h (h) 3 h (h) 4 h (h) 5 h (N) 1 Output #1 h (N) 2 Output #2 h (N) 3 Output #3 h (N) 4 Output #4 h (N) 5 Output #5 Hidden layer 1 Input layer Hidden layer h...

This paper presents a comparison study between the multilayer perceptron (MLP) and radial basis function (RBF) neural networks with supervised learning and back propagation algorithm to track hand gestures. Both networks have two output classes which are hand and face. Skin is detected by a regional based algorithm in the image, and then networks are applied on video sequences frame by frame in...

background: today, despite the many advances in early detection of diseases, cancer patients have a poor prognosis and the survival rates in them are low. recently, microarray technologies have been used for gathering thousands data about the gene expression level of cancer cells. these types of data are the main indicators in survival prediction of cancer. this study highlights the improvement...

In this work we study and develop learning algorithms for networks based on regularization theory. In particular, we focus on learning possibilities for a family of regularization networks and radial basis function networks (RBF networks). The framework above the basic algorithm derived from theory is designed. It includes an estimation of a regularization parameter and a kernel function by min...

Learning algorithms have been used both on feed-forward deterministic networks and on feed-back statistical networks to capture input-output relations and do pattern classification. These learning algorithms are examined for a class of problems characterized by noisy or statistical data, in which the networks learn the relation between input data and probability distributions of answers. In sim...