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[2411.18954] NeuroLifting: Neural Inference on Markov Random Fields at Scale
Summary
NeuroLifting introduces a novel approach for inference in large-scale Markov Random Fields (MRFs) using Graph Neural Networks, achieving superior solution quality and efficiency compared to traditional methods.
Why It Matters
This research addresses the limitations of existing inference methods for MRFs, which struggle with scalability and solution quality. By leveraging neural networks, NeuroLifting provides a promising solution that can enhance performance in various applications, particularly in machine learning and AI.
Key Takeaways
- NeuroLifting reparameterizes decision variables in MRFs using Graph Neural Networks.
- It enables efficient optimization through standard gradient descent techniques.
- Empirical results show it closely matches the exact solver Toulbar2 on moderate scales.
- On large-scale MRFs, NeuroLifting outperforms all baseline methods with linear computational complexity.
- This advancement offers a scalable solution for complex inference tasks in AI.
Computer Science > Machine Learning arXiv:2411.18954 (cs) [Submitted on 28 Nov 2024 (v1), last revised 17 Feb 2026 (this version, v3)] Title:NeuroLifting: Neural Inference on Markov Random Fields at Scale Authors:Yaomin Wang, Chaolong Ying, Xiaodong Luo, Tianshu Yu View a PDF of the paper titled NeuroLifting: Neural Inference on Markov Random Fields at Scale, by Yaomin Wang and 3 other authors View PDF HTML (experimental) Abstract:Inference in large-scale Markov Random Fields (MRFs) is a critical yet challenging task, traditionally approached through approximate methods like belief propagation and mean field, or exact methods such as the Toulbar2 solver. These strategies often fail to strike an optimal balance between efficiency and solution quality, particularly as the problem scale increases. This paper introduces NeuroLifting, a novel technique that leverages Graph Neural Networks (GNNs) to reparameterize decision variables in MRFs, facilitating the use of standard gradient descent optimization. By extending traditional lifting techniques into a non-parametric neural network framework, NeuroLifting benefits from the smooth loss landscape of neural networks, enabling efficient and parallelizable optimization. Empirical results demonstrate that, on moderate scales, NeuroLifting performs very close to the exact solver Toulbar2 in terms of solution quality, significantly surpassing existing approximate methods. Notably, on large-scale MRFs, NeuroLifting delivers superior so...
