no code implementations • 25 Apr 2023 • Van-Duc Le
Our previous research has solved the problem by considering the whole city as an image and leveraged a Convolutional Long Short-Term Memory (ConvLSTM) model to learn the spatiotemporal features.
no code implementations • 25 Apr 2023 • Van-Duc Le, Cuong-Tien Bui, Wen-Syan Li
Another critical issue is the model accuracy degradation by the difference between training data and testing data during the ML lifetime, which leads to lifecycle rebuild.
no code implementations • 3 Nov 2022 • Tien-Cuong Bui, Van-Duc Le, Wen-Syan Li, Sang Kyun Cha
Graph Neural Networks (GNNs) are widely used in many modern applications, necessitating explanations for their decisions.
no code implementations • 20 Oct 2022 • Tien-Cuong Bui, Van-Duc Le, Wen-Syan Li, Sang Kyun Cha
Therefore, we propose a novel GNN explanation framework named SCALE, which is general and fast for explaining predictions.
no code implementations • 17 Aug 2022 • Tung-Lam Duong, Van-Duc Le, Tien-Cuong Bui, Hai-Thien To
Although the smart camera parking system concept has existed for decades, a few approaches have fully addressed the system's scalability and reliability.
no code implementations • 29 Nov 2020 • Tien-Cuong Bui, Van-Duc Le, Hai-Thien To, Sang Kyun Cha
Paraphrase generation is a long-standing problem and serves an essential role in many natural language processing problems.
no code implementations • 29 Nov 2019 • Van-Duc Le, Tien-Cuong Bui, Sang Kyun Cha
In this research, we present many spatiotemporal datasets collected over Seoul city in Korea, which is currently much suffered by air pollution problem as well.
no code implementations • 21 Apr 2018 • Tien-Cuong Bui, Van-Duc Le, Sang-Kyun Cha
Tackling air pollution is an imperative problem in South Korea, especially in urban areas, over the last few years.