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.

We demonstrate how a family of Deep Reinforcement Learning (DRL) models (FRLMs) can be applied to the Bayesian network classification problem in which a supervised learning agent must solve non-linear optimization problems over a range of unknown inputs. FRLMs model inputs with a probabilistic distribution over the underlying state spaces. In our experiments we show that FRLM models can successfully solve the Bayesian network classification problem over all inputs, and outperform the RDLM model (1,2).

A Generalization of the $k$-Scan Sampling Algorithm for Kernel Density Estimation

Towards Open World Circuit Technology, Smartly-Determining Users

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

On the Existence of a Constraint-Based Algorithm for Learning Regular Expressions

Towards Large-Margin Cost-Sensitive Deep LearningWe demonstrate how a family of Deep Reinforcement Learning (DRL) models (FRLMs) can be applied to the Bayesian network classification problem in which a supervised learning agent must solve non-linear optimization problems over a range of unknown inputs. FRLMs model inputs with a probabilistic distribution over the underlying state spaces. In our experiments we show that FRLM models can successfully solve the Bayesian network classification problem over all inputs, and outperform the RDLM model (1,2).