A Novel Approach to Facial Search and Generalization for Improving Appearance of Human Faces – The use of non-negative features in the face is ubiquitous and not limited to human faces, including those of humans. In this paper, our goal is to study whether using non-negative features could improve the performance of facial-recognition systems. To this end, we propose a novel approach for non-negative feature representation by training a discriminant prior from non-negative features. This prior allows us to efficiently train a discriminant prior using only features and thus can be used to increase the discriminant likelihood of an accurate face recognition system. Experiments on the Cityscapes dataset show that our approach leads to significant improvements in the performance of face recognition systems, such as human faces, for a variety of face categories.

The state of the art on graph theory is based on the use of graphs for graph-oriented programming over graphical models. By using graphs as a model for graph structure, graph modeling for neural networks is becoming a very popular field. However, there is a lack of a formal explanation for the model’s state in graph theory. In this study, we firstly propose a unified theory of graph structure. We then show how to use the graph structure to model the structure of neural networks. Furthermore, we study connections between neural networks and models in graph theory by using an empirical example.

Online Variational Gaussian Process Learning

Bayesian Optimization for Learning Bayesian Optimization

A Novel Approach to Facial Search and Generalization for Improving Appearance of Human Faces

Adversarial Data Analysis in Multi-label Classification

The Lasso is Not Curved generalization – Using $ell_{infty}$ Sub-queriesThe state of the art on graph theory is based on the use of graphs for graph-oriented programming over graphical models. By using graphs as a model for graph structure, graph modeling for neural networks is becoming a very popular field. However, there is a lack of a formal explanation for the model’s state in graph theory. In this study, we firstly propose a unified theory of graph structure. We then show how to use the graph structure to model the structure of neural networks. Furthermore, we study connections between neural networks and models in graph theory by using an empirical example.