most citedHow Does Attention Work in Vision Transformers? A Visual Analytics Attempt

1 citations · 1 across the 2 of their papers we have counts for

collaborators

15 papers

cs.LG2024

PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies

Audrey Der, Chin-Chia Michael Yeh, Yan Zheng +5

In recent years there has been significant progress in time series anomaly detection. However, after detecting an (perhaps tentative) anomaly, can we explain it? Such explanations…

cs.DB2023

Time Series Synthesis Using the Matrix Profile for Anonymization

Audrey Der, Chin-Chia Michael Yeh, Yan Zheng +6

Publishing and sharing data is crucial for the data mining community, allowing collaboration and driving open innovation. However, many researchers cannot release their data due to…

cs.LG2023

Ego-Network Transformer for Subsequence Classification in Time Series Data

Chin-Chia Michael Yeh, Huiyuan Chen, Yujie Fan +8

Time series classification is a widely studied problem in the field of time series data mining. Previous research has predominantly focused on scenarios where relevant or foregroun…

cs.IR2023

Temporal Treasure Hunt: Content-based Time Series Retrieval System for Discovering Insights

Chin-Chia Michael Yeh, Huiyuan Chen, Xin Dai +8

Time series data is ubiquitous across various domains such as finance, healthcare, and manufacturing, but their properties can vary significantly depending on the domain they origi…

cs.LG202312 cited

FATA-Trans: Field And Time-Aware Transformer for Sequential Tabular Data

Dongyu Zhang, Liang Wang, Xin Dai +7

Sequential tabular data is one of the most commonly used data types in real-world applications. Different from conventional tabular data, where rows in a table are independent, seq…

cs.LG2023

Multitask Learning for Time Series Data with 2D Convolution

Chin-Chia Michael Yeh, Xin Dai, Yan Zheng +7

Multitask learning (MTL) aims to develop a unified model that can handle a set of closely related tasks simultaneously. By optimizing the model across multiple tasks, MTL generally…