Search Results for author: Seungmin Jin

Found 4 papers, 2 papers with code

A Visual Analytics System for Improving Attention-based Traffic Forecasting Models

no code implementations8 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.

Dynamic Time Warping

Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting

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.

An Empirical Experiment on Deep Learning Models for Predicting Traffic Data

no code implementations12 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.

ST-GRAT: A Novel Spatio-temporal Graph Attention Network for Accurately Forecasting Dynamically Changing Road Speed

1 code implementation29 Nov 2019 Cheonbok Park, Chunggi Lee, Hyojin Bahng, Yunwon Tae, Kihwan Kim, Seungmin Jin, Sungahn Ko, Jaegul Choo

Predicting road traffic speed is a challenging task due to different types of roads, abrupt speed change and spatial dependencies between roads; it requires the modeling of dynamically changing spatial dependencies among roads and temporal patterns over long input sequences.

Graph Attention

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