✨Model-Based and Sample-Efficient AI-Assisted Math Discovery in Sphere Packing
📝 Summary:
A model-based AI method using Bayesian optimization and MCTS improves sphere packing upper bounds for dimensions 4-16. It treats SDP construction as a sequential decision process, proving effective for sample-limited math discovery.
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04829
• PDF: https://arxiv.org/pdf/2512.04829
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For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #SpherePacking #MathDiscovery #Optimization #BayesianOptimization
📝 Summary:
A model-based AI method using Bayesian optimization and MCTS improves sphere packing upper bounds for dimensions 4-16. It treats SDP construction as a sequential decision process, proving effective for sample-limited math discovery.
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04829
• PDF: https://arxiv.org/pdf/2512.04829
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #SpherePacking #MathDiscovery #Optimization #BayesianOptimization