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arXiv AI By Salomon Ibarra, Frida Cantu, Kaixiong Zhou, Li Zhang

Gradient-based Model Shortcut Detection for Time Series Classification

Read the original on arXiv AI →

The paper "Gradient-based Model Shortcut Detection for Time Series Classification" investigates how deep neural networks (DNNs) can develop shortcut learning behaviors in time series classification tasks, where models may rely on spurious correlations in the training data. It introduces a gradient‑based detection method that identifies point‑based shortcuts without needing test data or clean training classes, and evaluates this approach on UCR time series datasets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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