Hierarchical Gaussian Process Models – Learning to predict future events is challenging because of the large, complex, and unpredictable nature of the data. Despite the enormous volume of available data, supervised learning has made great progress in recent years in learning to predict the future rather than in predicting the past. In this paper, we present a framework for modeling and predicting the future of data by non-Gaussian prior approximating latent Gaussian processes. The underlying assumptions are to be established in the context of non-Gaussian prior approximating learning, and we further elaborate on these assumptions in a neural-network architecture. We evaluate this network on two datasets: the Long Short-Term Memory and the Stanford Attention Framework dataset, where we show that the model achieves state-of-the-art performance with good accuracy.

There are several recent algorithms for predicting vehicles from data in traffic data streams. In particular, the use of the Lasso is based on solving a very difficult optimization problem, which involves constructing a model of a given data stream using a nonzero sum of the sum of the data. In this paper, we propose an algorithm that combines the optimization and data mining applications of Lasso: We first propose a simple algorithm, called T-LSTM, which is able to be used both as a preprocessing step for the optimisation of the prediction and as a preprocessing function for the optimization of the Lasso. We demonstrate the importance of this approach on the CityScape dataset, and demonstrate several methods for predicting vehicles using T-LSTM.

MorphNet: A Deep Neural Network for Automated Identification

Scalable and Accurate Vehicle Acceleration via Adversarial Attack on Deep Learning Training Data

# Hierarchical Gaussian Process Models

A Geometric Framework for 3D Object Classification from Semi-triangulation

The Application of Bayesian Network Techniques for Vehicle Speed ForecastingThere are several recent algorithms for predicting vehicles from data in traffic data streams. In particular, the use of the Lasso is based on solving a very difficult optimization problem, which involves constructing a model of a given data stream using a nonzero sum of the sum of the data. In this paper, we propose an algorithm that combines the optimization and data mining applications of Lasso: We first propose a simple algorithm, called T-LSTM, which is able to be used both as a preprocessing step for the optimisation of the prediction and as a preprocessing function for the optimization of the Lasso. We demonstrate the importance of this approach on the CityScape dataset, and demonstrate several methods for predicting vehicles using T-LSTM.