Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Tensor Belief Propagation
Authors: Andrew Wrigley, Wee Sun Lee, Nan Ye
ICML 2017 | Venue PDF | LLM Run Details | Input Tokens: 15,923 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,252 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |