Tensor Belief Propagation
Authors: Andrew Wrigley, Wee Sun Lee, Nan Ye
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare our algorithm experimentally with several approximate inference algorithms and show that it performs well. We present experiments on grid-structured Ising models, random graphs with pairwise Ising potentials, and two real-world datasets from the UAI 2014 Inference Competition (Gogate, 2014). |
| Researcher Affiliation | Academia | 1Australian National University, Canberra, Australia. 2National University of Singapore, Singapore. 3Queensland University of Technology, Brisbane, Australia. |
| Pseudocode | Yes | Algorithm 1 Tensor Belief Propagation |
| Open Source Code | No | The paper states that TBP was implemented in C++ inside the lib DAI framework, but does not provide a link or explicit statement that their specific implementation of TBP is open source. |
| Open Datasets | Yes | We present experiments on grid-structured Ising models, random graphs with pairwise Ising potentials, and two real-world datasets from the UAI 2014 Inference Competition (Gogate, 2014). UAI 2014 Inference Competition. http://www.hlt.utdallas.edu/ vgogate/ uai14-competition/index.html, 2014. |
| Dataset Splits | No | The paper does not explicitly specify training, validation, and test dataset splits for the experiments. It describes how models are generated or uses pre-defined competition problem instances rather than splitting a dataset. |
| Hardware Specification | Yes | all tests were executed on a single core of a 1.4 GHz Intel Core i5 processor. |
| Software Dependencies | Yes | TBP was implemented in C++ inside the lib DAI framework using Eigen (Guennebaud et al., 2010). lib DAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models. Journal of Machine Learning Research, 11: 2169–2173, August 2010. Eigen v3. http://eigen.tuxfamily.org, 2010. |
| Experiment Setup | Yes | In our experiments, we choose the wij uniformly from [-2, 2] (mixed interactions) or [0, 2] (attractive interactions), and the bi uniformly from [-1, 1]. We use a symmetric rank-2 tensor decomposition for the pairwise potentials. Parameters used for BP, MF, TRW and Gibbs are given in the supplementary material. The initial potential functions are decomposed into mixtures with r components. We show results for r = 2 and r = 4. |