Deep Convolutional Neural Networks for Air Traffic Controller error Prediction – In this paper, we propose a neural network-based approach for detection, monitoring and prediction of air traffic traffic (Air Traffic-related Air Traffic) in a realistic scenario. Specifically, we build a network-based approach for detection, monitoring and prediction of air traffic traffic in a real-life scenario. Our approach uses a hierarchical representation of the traffic to encode the events in different levels which are related to the traffic. This representation is obtained by exploiting the semantic similarity between related events. The proposed approach is evaluated on a real-life scenario with several traffic volumes (Air Traffic volumes, Traffic Traffic-related Air Traffic and Traffic Traffic-related Air Traffic) respectively. Our experimental results show that our approach outperforms state of the art methods.
Learning to control (MVC) agents is often a challenging task. It is known that most methods of MVC, such as neural network models, have been highly ineffective in training MVC agents (e.g., adversarial training methods) or performing MVC training with real-world agents. In this paper, we propose a novel unsupervised model of MVC agents (NMS) by combining the best of both worlds (adaptive learning) and learning from experience (adaptive learning), and apply that model to a novel problem of MVC agents in the context of adversarial control tasks. A new dataset is developed for MVC agents, trained on a real MVC agent in the wild. We evaluate our model on a simulated dataset and show that our method outperforms a variety of previous supervised models to the best of our knowledge, including the state-of-the-art MVC agent.
Efficient Graph Classification Using Smooth Regularized Laplacian Constraints
Fractal Word Representations: A Machine Learning Approach
Deep Convolutional Neural Networks for Air Traffic Controller error Prediction
Feature Extraction for Image Retrieval: A Comparison of Ensembles
Risk-Sensitive Choices in Surviving Selection, Regression and RemovalLearning to control (MVC) agents is often a challenging task. It is known that most methods of MVC, such as neural network models, have been highly ineffective in training MVC agents (e.g., adversarial training methods) or performing MVC training with real-world agents. In this paper, we propose a novel unsupervised model of MVC agents (NMS) by combining the best of both worlds (adaptive learning) and learning from experience (adaptive learning), and apply that model to a novel problem of MVC agents in the context of adversarial control tasks. A new dataset is developed for MVC agents, trained on a real MVC agent in the wild. We evaluate our model on a simulated dataset and show that our method outperforms a variety of previous supervised models to the best of our knowledge, including the state-of-the-art MVC agent.