High-performance hybrid neural network for Alzheimer disease biomarker generation – Neural systems with recurrent neural networks are highly sensitive to environmental noise and to noise-related activity patterns. In particular it is important to study the performance of neural systems trained for a variety of tasks, e.g. text detection, image-visualization of text, and text generation.
Understanding the interplay of a sequence of motion sequences can be an important resource for autonomous vehicle navigation. One of the challenges in tracking such a sequence is that the motion is not accurately captured, i.e., the time is too short or long to allow proper tracking. In this paper, we propose a learning based method for tracking motion sequences. A tracking network is trained with a video sequence, and a set of objects is automatically captured by a robot. The robot then tracks objects in the video sequence. As an end-to-end learning method, our method requires a video-based data augmentation method. The learning method is applied to three different tracking strategies: tracking motion sequences without data augmentation, tracking motion sequences without video augmentation, and tracking motion sequences without video augmentation. The results show that our approach significantly outperforms the previous methods on a variety of tracking scenarios without data augmentation.
An Expectation-Propagation Based Approach for Transfer Learning of Reinforcement Learning Agents
A Multi-level Non-Rigid Image Registration Using anisotropic Diffusion
High-performance hybrid neural network for Alzheimer disease biomarker generation
The R Package K-Nearest Neighbor for Image Matching
A Framework for Interactive Vehicle Detection and Localization in Video with Event-Part InteractionsUnderstanding the interplay of a sequence of motion sequences can be an important resource for autonomous vehicle navigation. One of the challenges in tracking such a sequence is that the motion is not accurately captured, i.e., the time is too short or long to allow proper tracking. In this paper, we propose a learning based method for tracking motion sequences. A tracking network is trained with a video sequence, and a set of objects is automatically captured by a robot. The robot then tracks objects in the video sequence. As an end-to-end learning method, our method requires a video-based data augmentation method. The learning method is applied to three different tracking strategies: tracking motion sequences without data augmentation, tracking motion sequences without video augmentation, and tracking motion sequences without video augmentation. The results show that our approach significantly outperforms the previous methods on a variety of tracking scenarios without data augmentation.