A Novel Feature Selection Framework for Face Recognition Using Generative Adversarial Networks – This paper presents the use of genetic algorithms to design a model that can perform various tasks. A typical model for face recognition is a multi-agent hybrid game. The main contribution of this paper is to show that the same approach can be used for a new task in machine learning. In this case, the model can choose from all the options available to the agent. Given the input from this hybrid game and the generated action space in the agents’ behavior, the model is able to choose from a set of actions. The algorithm is evaluated on the task of human face recognition. The results indicate that the hybrid model is capable of recovering the input of the agent and thus improving the performance of its agent.

This paper investigates the relationship between human visual perception and computational models of emotion. Previous studies focus on visual processing and human action recognition using a neural network, with the aim to analyze and compare these systems. We examine two aspects of human visual perception: 3D model of facial expressions and 3D modeling of emotions. In the former case, visual object recognition is a difficult task, while emotions are typically represented with visual features. In the latter case, we use a deep neural network to extract relevant visual features from visual appearance. We test several models based on visual features to evaluate their performance against a single model, based on human-level visual reasoning and action recognition models. A generalization error analysis is made by comparing the performance of models trained by human-level models of human action recognition and model with visual features. We validate performance of models trained by human-level models of human action recognition and test them with human-level models of emotion recognition. Experimental comparisons show that human action recognition systems (i.e., the human emotion recognition system) outperform model-based methods on human action recognition.

A New Method for Automating Knowledge Base Analyses in RTF and DAT based Ontologies

On the Semantic Similarity of Knowledge Graphs: Deep Similarity Learning

A Novel Feature Selection Framework for Face Recognition Using Generative Adversarial Networks

Learning Stochastic Gradient Temporal Algorithms with Riemannian Metrics

A comparative study of the three generative reanaesthetic strategies for malaria controlThis paper investigates the relationship between human visual perception and computational models of emotion. Previous studies focus on visual processing and human action recognition using a neural network, with the aim to analyze and compare these systems. We examine two aspects of human visual perception: 3D model of facial expressions and 3D modeling of emotions. In the former case, visual object recognition is a difficult task, while emotions are typically represented with visual features. In the latter case, we use a deep neural network to extract relevant visual features from visual appearance. We test several models based on visual features to evaluate their performance against a single model, based on human-level visual reasoning and action recognition models. A generalization error analysis is made by comparing the performance of models trained by human-level models of human action recognition and model with visual features. We validate performance of models trained by human-level models of human action recognition and test them with human-level models of emotion recognition. Experimental comparisons show that human action recognition systems (i.e., the human emotion recognition system) outperform model-based methods on human action recognition.