1 code implementation • 20 Oct 2022 • Jihoon Ko, Kyuhan Lee, Hyunjin Hwang, Kijung Shin
Recently, many deep-learning techniques have been applied to various weather-related prediction tasks, including precipitation nowcasting (i. e., predicting precipitation levels and locations in the near future).
no code implementations • 17 Feb 2022 • Jihoon Ko, Kyuhan Lee, Hyunjin Hwang, Seok-Geun Oh, Seok-Woo Son, Kijung Shin
It is highlighted that our pre-training scheme and new loss function improve the critical success index (CSI) of nowcasting of heavy rainfall (at least 10 mm/hr) by up to 95. 7% and 43. 6%, respectively, at a 5-hr lead time.
no code implementations • 19 Oct 2020 • Houquan Zhou, Shenghua Liu, Kyuhan Lee, Kijung Shin, HuaWei Shen, Xueqi Cheng
As a solution, graph summarization, which aims to find a compact representation that preserves the important properties of a given graph, has received much attention, and numerous algorithms have been developed for it.
Social and Information Networks
2 code implementations • 1 Jun 2020 • Kyuhan Lee, Hyeonsoo Jo, Jihoon Ko, Sungsu Lim, Kijung Shin
SSumM not only merges nodes together but also sparsifies the summary graph, and the two strategies are carefully balanced based on the minimum description length principle.
Databases Social and Information Networks H.2.8
1 code implementation • 24 Jan 2020 • Jihoon Ko, Kyuhan Lee, Kijung Shin, Noseong Park
In this work, we present an inductive machine learning method, called Monte Carlo Simulator (MONSTOR), for estimating the influence of given seed nodes in social networks unseen during training.
