FAME: Scalable Minimal NN Explanations

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📄Read original on ArXiv AI

#explainable-ai#neural-explanationsfamefamelirpaverix+arxiv

💡Scales minimal XAI to large NNs, beats VERIX+ on size/speed (arXiv new).

⚡ 30-Second TL;DR

What Changed

First scalable method for minimal explanations on large neural networks

Why It Matters

Advances explainable AI by enabling formal, minimal explanations for large NNs, aiding trust in high-stakes deployments. Reduces explanation complexity, making XAI more practical for real-world use.

What To Do Next

Download arXiv:2603.10661 and implement FAME for minimal explanations on your neural nets.

Who should care:Researchers & Academics

Key Points

•First scalable method for minimal explanations on large neural networks
•Dedicated perturbation domains eliminate traversal order dependency
•Progressive domain shrinking with LiRPA bounds discards irrelevant features
•New evaluation measures worst-case distance to true minimal explanations
•Outperforms VERIX+ in explanation size and runtime

🧠 Deep Insight

Background and context from public sources — not the original article. 9 sources cited.

🔑 Enhanced Key Takeaways

•FAME paper was submitted to arXiv on March 11, 2026, by authors Ryma Boumazouza, Raya Elsaleh, Melanie Ducoffe, Shahaf Bassan, and Guy Katz from the Hebrew University of Jerusalem.[2][7][8]
•FAME is accepted or to appear at the 14th International Conference on Learning Representations (ICLR) in 2026.[6][8][9]
•Authors Shahaf Bassan and Guy Katz have a history of publications on neural network interpretability and verification, including works at CAV 2024 and ECAI 2024 on computational hardness of explanations and local vs. global interpretability.[7][8]

🔮 Future ImplicationsAI analysis grounded in cited sources

FAME will be presented at ICLR 2026

Multiple sources reference the paper as accepted to ICLR 2026, indicating upcoming conference presentation.[6][8][9]

⏳ Timeline

2026-03

FAME paper submitted to arXiv (v1)

2026-02

FAME referenced in related arXiv paper on neural additive models

2025-07

Katz Lab publishes on ensemble interpretability at ICML

2024-10

Katz Lab publishes on computational hardness of model interpretation at ECAI

2024-07

Katz Lab publishes on local vs. global interpretability at CAV

📎 Sources (9)

Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.

📄Read original article on ArXiv AI

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