On the Consequences of a Batch Size Predictive Modelling Approach – This paper addresses the problem of learning a set of optimal functions through sequential decision support (SRS). The first problem is to identify a set of most likely functions satisfying the SRS. This is the main approach of some of the literature on SRS. This problem is very challenging because it may have many related problems. In particular, given a sequential decision support (SRS), the choice to choose a function or an optimal function may be of very large importance. In this paper, we explore this problem with the help of a sequential SRS algorithm known as the Decision-Supporting SRS (DAST). This approach aims to identify the most likely functions which satisfy the SRS. By this algorithm, we also propose a set of functions which are suitable for the SRS. The proposed algorithms are evaluated on several synthetic datasets and our results show that our approach is significantly faster when compared with the state-of-the-art algorithms.
We propose a deep learning-based approach for extracting high-quality texture images of a scene from a large texture dataset. Our approach is trained on a texture dataset, and further trained on the deep network on a smaller dataset. For training, we train deep network to extract rich texture features and then use an algorithm based on the discriminative loss to classify the texture features. We show that our approach can significantly reduce the number of iterations required for training, and outperforms previous methods in image classification.
A Novel Approach for Improved Noise Robust to Speckle and Noise Sensitivity
On the Consequences of a Batch Size Predictive Modelling Approach
Deep Learning for Realtime Road Scattering by Generating Semantic Shapes on a Massive Texture NetworkWe propose a deep learning-based approach for extracting high-quality texture images of a scene from a large texture dataset. Our approach is trained on a texture dataset, and further trained on the deep network on a smaller dataset. For training, we train deep network to extract rich texture features and then use an algorithm based on the discriminative loss to classify the texture features. We show that our approach can significantly reduce the number of iterations required for training, and outperforms previous methods in image classification.