1 code implementation • 5 Mar 2024 • Hyunwook Lee, Sungahn Ko
In this paper, we propose a novel deep learning model named TESTAM, which individually models recurring and non-recurring traffic patterns by a mixture-of-experts model with three experts on temporal modeling, spatio-temporal modeling with static graph, and dynamic spatio-temporal dependency modeling with dynamic graph.
no code implementations • 1 Aug 2023 • Hyeon Jeon, Ghulam Jilani Quadri, Hyunwook Lee, Paul Rosen, Danielle Albers Szafir, Jinwook Seo
In this research, we study perceptual variability in conducting visual clustering, which we call Cluster Ambiguity.
1 code implementation • 26 Oct 2022 • Hyunwook Lee, Chunggi Lee, Hongkyu Lim, Sungahn Ko
In this paper, we examine the definition of shape and distortions, which are crucial for shape-awareness in time-series forecasting, and provide a design rationale for the shape-aware loss function.
no code implementations • 8 Aug 2022 • Seungmin Jin, Hyunwook Lee, Cheonbok Park, Hyeshin Chu, Yunwon Tae, Jaegul Choo, Sungahn Ko
With deep learning (DL) outperforming conventional methods for different tasks, much effort has been devoted to utilizing DL in various domains.
1 code implementation • ICLR 2022 • Hyunwook Lee, Seungmin Jin, Hyeshin Chu, Hongkyu Lim, Sungahn Ko
To evaluate the validness of the new perspective, we design a novel traffic forecasting model, called Pattern-Matching Memory Networks (PM-MemNet), which learns to match input data to the representative patterns with a key-value memory structure.
no code implementations • 12 May 2021 • Hyunwook Lee, Cheonbok Park, Seungmin Jin, Hyeshin Chu, Jaegul Choo, Sungahn Ko
For example, it is difficult to figure out which models provide state-of-the-art performance, as recently proposed models have often been evaluated with different datasets and experiment environments.