Inference in Graphical Models via Semidefinite Programming Hierarchies
Authors: Murat A. Erdogdu, Yash Deshpande, Andrea Montanari
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 4, we present numerical experiments with PSOS by solving problems of size up to 10, 000 within several minutes. |
| Researcher Affiliation | Collaboration | Murat A. Erdogdu Microsoft Research erdogdu@cs.toronto.edu Yash Deshpande MIT and Microsoft Research yash@mit.edu Andrea Montanari Stanford University montanari@stanford.edu |
| Pseudocode | Yes | Algorithm 1: Partial-SOS, Algorithm 2: CLAP: Confidence Lift And Project |
| Open Source Code | No | The paper does not provide any concrete statement or link regarding the public availability of the source code for the described methodology. |
| Open Datasets | No | The paper uses generated data for image denoising (100x100 binary images with Bernoulli or blockwise noise) and Ising spin glasses (two-dimensional grids with i.i.d. parameters), but does not provide specific access information (link, DOI, formal citation) to a publicly available dataset for reproduction. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or citations to predefined splits) for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers required to replicate the experiments. |
| Experiment Setup | Yes | In applying Algorithm 1, we add diagonals to the grid (see right plot in Figure 1) in order to satisfy the condition (3.1) with corresponding weight e_d. The model parameter 0 is chosen in each case as to approximately optimize the performances under BP denoising. We use an inertia of 0.5 to help convergence [YFW05], and threshold the marginals at 0.5. Similar to BP-SP, we use an inertia of 0.5. We use plaquettes in the grid (contiguous groups of four vertices) as the largest regions, and apply message passing with inertia 0.1 [WF01]. We find that there is little or no improvement beyond r = 10 (cf. Figure 2). |