A Novel Hybrid Approach for Fast Learning of Temporal Sequences (Extended Bi-Directional Wavelet Features) from Sequences – This paper proposes a novel learning technique for extracting novel features in an image with a non-Gaussian background. A non-Gaussian background is a smooth and irregular image object with multiple discrete or continuous objects or groups of objects. The underlying structure of the non-Gaussian background can be inferred from the structure of the target object. The data is obtained using an image segmentation method and a structured prediction criterion. The proposed technique is evaluated on three different datasets: a standard benchmark dataset, the PASCAL-D dataset (a collection of more than 4500 images collected using a machine learning or computer vision framework), and CIFAR-10, a standard benchmark dataset. The experimental results show that the proposed model outperforms the state-of-the-art methods by a large margin on both (1) the PASCAL-D dataset and (2) the CIFAR-10 dataset.

The ability to control a vehicle with only a camera still allows for accurate, accurate and efficient driving in some scenarios, but the human driver of a vehicle needs to be able to make informed control decisions given the available ground truth. The use of human-based vehicles as an example to illustrate the potential value and usefulness of deep reinforcement learning could benefit a lot of other research.

Deep Learning of Spatio-temporal Event Knowledge with Recurrent Neural Networks

Convex Penalized Bayesian Learning of Markov Equivalence Classes

A Novel Hybrid Approach for Fast Learning of Temporal Sequences (Extended Bi-Directional Wavelet Features) from Sequences

A novel approach to natural language generation

Hierarchical Image Classification Using 3D Deep Learning for Autonomous DrivingThe ability to control a vehicle with only a camera still allows for accurate, accurate and efficient driving in some scenarios, but the human driver of a vehicle needs to be able to make informed control decisions given the available ground truth. The use of human-based vehicles as an example to illustrate the potential value and usefulness of deep reinforcement learning could benefit a lot of other research.