Explainable embeddings with Distance Explainer

1. 1Adapt saliency-based techniques from RISE to explain distance between embedded data points
2. 2Assign attribution to dimensions in the embedded space
3. 3Generate local, post-hoc explanations of embedded spaces in machine learning models
4. 4Apply Distance Explainer to real-world datasets to evaluate its effectiveness

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

💡 Distance Explainer provides a novel method for generating local explanations of embedded spaces in machine learning models

🚀 Explainable embeddings with Distance Explainer! 🤖