Deep learning has achieved great success in recent years in various areas of machine learning, but it requires an enormous amount of data for training. Data collection techniques such as web crawling have been developed to solve this problem, but these techniques run the risk of generating incorrect labels. When a deep learning model is trained for image classification problem with a dataset that ...Show More
In recent years, deeper and wider neural networks have shown excellent performance in computer vision tasks, while their enormous amount of parameters results in increased computational cost and overfitting. Several methods have been proposed to compress the size of the networks without reducing network performance. Network pruning can reduce redundant and unnecessary parameters from a network. Kn...Show More
In this work, we will consider the dimension reduction of the set of time series, such as economic data, to find the meaningful basis vector for the set of data, and indicate which data use which basis vector. Usually each of the time series is analyzed independently in economics but here we will analyze the set of time series simultaneously. Since some of the economic data are measured as positiv...Show More