Towards A Unified Information Bottleneck Framework for Time Series Explanations
Explaining deep learning models operating on time series data is crucial in various applications that require transparent and interpretable insights into model behavior. {Existing explanation methods generally fall into two categories: attribution-based explanations, which identify the temporal regions most responsible for a prediction, and counterfactual explanations, which reveal how an input…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.
