Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Regularized Gaussian Belief Propagation with Nodes of Arbitrary Size
Authors: Francois Kamper, Sarel J. Steel, Johan A. du Preez
JMLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Empirical Results In this section we present three empirical studies of the s Ga BP-m algorithm. In the first, we compare s Ga BP-m to the multivariate extensions of RGa BP and convergence fix Ga BP (CFGa BP) by considering both convergence speed and inference quality. |
| Researcher Affiliation | Academia | Francois Kamper EMAIL Sarel J. Steel EMAIL Department of Statistics and Actuarial Science Stellenbosch University Stellenbosch, South Africa Johan A. du Preez EMAIL Department of Electrical and Electronic Engineering Stellenbosch University Stellenbosch, South Africa |
| Pseudocode | Yes | Algorithm 1 Synchronous s Ga BP-m, Algorithm 2 Synchronous RGa BP, Algorithm 3 Synchronous CFGa BP |
| Open Source Code | No | No explicit statement about the availability of source code, a link to a repository, or code in supplementary materials is provided in the paper. |
| Open Datasets | No | We simulated data using the following procedure: (Section 5.1), The following simulation procedure was used: (Section 5.3) |
| Dataset Splits | No | This process was repeated 1 000 times. (Section 5.1), The above procedure was applied 1 000 times. (Section 5.3) This indicates repeated simulations, not dataset splits. |
| Hardware Specification | No | No specific hardware details (such as GPU/CPU models, processor types, or memory) used for running experiments are provided in the paper. |
| Software Dependencies | No | No specific software dependencies, libraries, or their version numbers are mentioned in the paper. |
| Experiment Setup | Yes | For each of s Ga BP-m, RGa BP and CFGa BP, we determine the hyperparameters yielding convergence in the minimum number of iterations using a line search with increments of 0.01. These parameters are then used to initialize the methods. (Section 5.1) For each application of the heuristic, we start with λ = 0 and consider using α = 0.01, 0.05 and 0.1. (Section 5.2) s Ga BP and s Ga BP-m were given 50 iterations to provide approximate univariate marginals. (Section 5.3) |