Explainable embeddings with Distance Explainer
📰 ArXiv cs.AI
Distance Explainer generates local explanations for embedded vector spaces in machine learning models
Action Steps
- Adapt saliency-based techniques from RISE to explain distance between embedded data points
- Assign attribution to dimensions in the embedded space
- Generate local, post-hoc explanations of embedded spaces in machine learning models
- Apply Distance Explainer to real-world datasets to evaluate its effectiveness
Who Needs to Know This
ML researchers and engineers benefit from this method as it provides interpretability in embedded vector spaces, allowing for better understanding and improvement of their models
Key Insight
💡 Distance Explainer provides a novel method for generating local explanations of embedded spaces in machine learning models
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🚀 Explainable embeddings with Distance Explainer! 🤖
Key Takeaways
Distance Explainer generates local explanations for embedded vector spaces in machine learning models
Full Article
Title: Explainable embeddings with Distance Explainer
Abstract:
arXiv:2505.15516v2 Announce Type: replace-cross Abstract: While eXplainable AI (XAI) has advanced significantly, few methods address interpretability in embedded vector spaces where dimensions represent complex abstractions. We introduce Distance Explainer, a novel method for generating local, post-hoc explanations of embedded spaces in machine learning models. Our approach adapts saliency-based techniques from RISE to explain the distance between two embedded data points by assigning attributio
Abstract:
arXiv:2505.15516v2 Announce Type: replace-cross Abstract: While eXplainable AI (XAI) has advanced significantly, few methods address interpretability in embedded vector spaces where dimensions represent complex abstractions. We introduce Distance Explainer, a novel method for generating local, post-hoc explanations of embedded spaces in machine learning models. Our approach adapts saliency-based techniques from RISE to explain the distance between two embedded data points by assigning attributio
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