most citedAbstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classification

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cs.LG2025

Graph Concept Bottleneck Models

Haotian Xu, Tsui-Wei Weng, Lam M. Nguyen +1

Concept Bottleneck Models (CBMs) provide explicit interpretations for deep neural networks through concepts and allow intervention with concepts to adjust final predictions. Existi…

cs.LG2024★ 1 cited

Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classification

Yunshi Wen, Tengfei Ma, Tsui-Wei Weng +2

In time-series analysis, many recent works seek to provide a unified view and representation for time-series across multiple domains, leading to the development of foundation model…