CENDRe: Concept Extraction with Natural Domain Representations
Researchers propose CENDRe, a concept extraction method for time-series models that captures both temporal and frequency patterns without predefined concept counts.
- CENDRe extracts interpretable concepts from time-series CNNs by analyzing both temporal and frequency-domain latent space representations.
- The method avoids predefined concept counts and improves localization alignment with model decision regions.
- Aims to enhance interpretability in critical domains like healthcare and finance.
- Published as an arXiv preprint, pending peer review.
A new paper introduces CENDRe, a concept extraction framework designed to improve the interpretability of convolutional neural networks (CNNs) used in time-series classification. Unlike prior methods that focus solely on time-domain features, CENDRe analyzes both temporal and spectral patterns within the model's latent space. This dual-domain approach aims to provide clearer insights into how CNNs make predictions in critical applications.
The method also eliminates the need to predefine the number of concepts, a common constraint in existing techniques. Additionally, CENDRe improves localization accuracy by aligning extracted concepts with the regions the model actually uses for decision-making. These advancements could enhance trust and transparency in AI systems deployed in fields like healthcare and finance, where interpretability is crucial.
The research is available as a preprint on arXiv, marking a step toward more explainable time-series AI models.
Provides a tool to improve model interpretability and debugging for time-series CNNs.
Could increase trust in AI-driven decision-making in regulated industries.
Offers insights into advanced techniques for explainable AI in time-series analysis.
Advances methods for understanding how AI models make predictions in critical applications.
- Concept Extraction (CE)
- A technique to identify and interpret patterns within a model's latent space that influence its predictions.
- Latent Space
- The internal representation space of a neural network where input data is transformed into abstract features.
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