Autonomous Navigation in Urban Area using Spatio-Temporal Traffic Modeling – We present a new technique for predicting future movements based on the spatial-temporal information of the environment. Our approach employs a Convolutional Neural Network (CNN), to predict the appearance of the environment. With this new approach, the CNN can simultaneously learn to predict the current state and predict future states from a previous state, thus providing a direct representation to the environment. Based on the prediction, the CNN computes a prediction score based on the current state and a posterior distribution to estimate the future state. This gives the CNN a better model for predictability. We demonstrate the use of these spatial and temporal cues in several real-world applications. The proposed approach is a very promising candidate for future state prediction in traffic and autonomous vehicles.

The concept of high-rank matrix is well developed in machine learning. It is used to make an efficient, low-rank matrix representation of data. In practice, matrix approximation problems are largely solved by hand. This paper provides a comprehensive analysis of matrix approximation algorithms that are not well-suited for low-rank matrices. This research is aimed at providing a new perspective on matrix approximation methods, focusing on the case in which the matrix is given by a few matrix approximators whose accuracy is well-suited for low-rank matrices. A new approach based on the non-Newton ratio approximators is proposed, which provides both efficient and efficient matrix approximation algorithms. The algorithm is shown to be very effective even for small matrix approximators.

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

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

Autonomous Navigation in Urban Area using Spatio-Temporal Traffic Modeling

On the Semantic Similarity of Knowledge Graphs: Deep Similarity Learning

Learning Efficient Algorithms for Learning Low Rank Matrices with Log-Orthogonal IterationThe concept of high-rank matrix is well developed in machine learning. It is used to make an efficient, low-rank matrix representation of data. In practice, matrix approximation problems are largely solved by hand. This paper provides a comprehensive analysis of matrix approximation algorithms that are not well-suited for low-rank matrices. This research is aimed at providing a new perspective on matrix approximation methods, focusing on the case in which the matrix is given by a few matrix approximators whose accuracy is well-suited for low-rank matrices. A new approach based on the non-Newton ratio approximators is proposed, which provides both efficient and efficient matrix approximation algorithms. The algorithm is shown to be very effective even for small matrix approximators.