Dense Learning for Robust Road Traffic Speed Prediction – State-of-the-art methods have focused on solving an optimization problem that is often a stationary problem. This work investigates the non-stationary problem in a non-stationary scenario. In this paper, we present two algorithms for the problem in which we do not believe that it is stationary. We also give an example of one method which does not support the non-stationary case and in which we believe that the problem is stationary that is solved as a linear program. We then provide an experimental evaluation on a real example.
Analogue video data are large data for many applications including social media and social media. In this work, we first investigate the existence of an analogue video dataset which can be used to construct a large dataset of the videos of human activities. We show that a deep convolutional neural network (CNN) can learn to extract and reuse relevant temporal information of the videos. We also show that a deep learning approach that automatically extracts information based on previous frames of the video can be used to model the current moment’s content and thus improve the learnt similarity between different videos in the same video context. We evaluate the proposed approach by a series of quantitative experiments, comparing it to a CNN trained on the real-world videos produced by human action recognition applications. The results show that using an analogue video dataset can lead to the best performance in human actions recognition on four benchmark domains.
Dynamic Metric Learning with Spatial Neural Networks
Quantum singularities used as an approximate quantum hard rule for decision making processes
Dense Learning for Robust Road Traffic Speed Prediction
A Supervised Deep Learning Approach to Reading Comprehension
Unsupervised Learning from Analogue Videos via Meta-LearningAnalogue video data are large data for many applications including social media and social media. In this work, we first investigate the existence of an analogue video dataset which can be used to construct a large dataset of the videos of human activities. We show that a deep convolutional neural network (CNN) can learn to extract and reuse relevant temporal information of the videos. We also show that a deep learning approach that automatically extracts information based on previous frames of the video can be used to model the current moment’s content and thus improve the learnt similarity between different videos in the same video context. We evaluate the proposed approach by a series of quantitative experiments, comparing it to a CNN trained on the real-world videos produced by human action recognition applications. The results show that using an analogue video dataset can lead to the best performance in human actions recognition on four benchmark domains.