ReMAP: Neural Reparameterization for Scalable MAP Inference in Arbitrary-Order Markov Random Fields

📰 ArXiv cs.AI

Learn how ReMAP enables scalable MAP inference in arbitrary-order Markov Random Fields using neural reparameterization, crucial for efficient probabilistic modeling

advanced Published 9 May 2026

Action Steps

Apply ReMAP to arbitrary-order MRFs to optimize MAP inference
Use neural reparameterization to relax the original MRF energy
Implement a differentiable relaxation of the MRF energy for efficient optimization
Compare ReMAP with approximate message-passing methods and exact solvers for scalability and accuracy
Configure ReMAP for instance-wise optimization to adapt to varying problem sizes

Who Needs to Know This

Machine learning engineers and researchers working with Markov Random Fields can benefit from ReMAP to improve the efficiency and scalability of their models, especially in dense or high-order instances

Key Insight

💡 ReMAP enables efficient and scalable MAP inference in arbitrary-order Markov Random Fields by leveraging neural reparameterization

Key Takeaways

Learn how ReMAP enables scalable MAP inference in arbitrary-order Markov Random Fields using neural reparameterization, crucial for efficient probabilistic modeling

Full Article

Title: ReMAP: Neural Reparameterization for Scalable MAP Inference in Arbitrary-Order Markov Random Fields

Abstract:
arXiv:2411.18954v4 Announce Type: replace-cross Abstract: Scalable high-quality MAP inference in arbitrary-order Markov Random Fields (MRFs) remains challenging. Approximate message-passing methods are often efficient but can degrade on dense or high-order instances, while exact solvers such as Toulbar2 become increasingly expensive at scale. We present ReMAP, an instance-wise neural reparameterization framework that directly optimizes a differentiable relaxation of the original MRF energy. Inst

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