Exactness of Approximate MAP Inference in Continuous MRFs
Authors: Nicholas Ruozzi
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we use graph covers to provide necessary and sufficient conditions for continuous MAP relaxations to be tight. We use this characterization to give simple proofs that the relaxation is tight for log-concave decomposable and logsupermodular decomposable models. Apart from this characterization theorem, the primary goal of this work is to move towards a uniform treatment of the discrete and continuous cases; they are not as different as they may initially appear. The proof of Theorem 3.1 is conceptually straightforward, albeit technical, and can be found in Appendix A. |
| Researcher Affiliation | Academia | Nicholas Ruozzi Department of Computer Science University of Texas at Dallas Richardson, TX 75080 |
| Pseudocode | No | The paper contains mathematical derivations and proofs, but no pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for the described methodology. |
| Open Datasets | No | This paper is theoretical and does not use or reference any datasets. |
| Dataset Splits | No | This paper is theoretical and does not describe or use any dataset splits for validation. |
| Hardware Specification | No | This paper is theoretical and does not conduct experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | This paper is theoretical and does not conduct experiments, therefore no specific software dependencies are mentioned. |
| Experiment Setup | No | This paper is theoretical and does not conduct experiments, therefore no experimental setup details are provided. |