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.
Putting MRFs on a Tensor Train
Authors: Alexander Novikov, Anton Rodomanov, Anton Osokin, Dmitry Vetrov
ICML 2014 | Venue PDF | LLM Run Details | Input Tokens: 16,934 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,031 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 evaluate our methods on several datasets and compare them against several state-of-the-art techniques. We report a significant improvement in the problems of the estimation of the partition function and the marginal distributions and show comparable results in the MAP-inference problem. (Abstract) ... 7. Experiments In all our experiments we use a non-optimized MATLAB implementation3 of our methods. |
| Researcher Affiliation | Academia | 1 Moscow State University, Moscow, Russia 2 Higher School of Economics, Moscow, Russia |
| Pseudocode | Yes | Algorithm 1 Compute the partition function Z |
| Open Source Code | Yes | https://github.com/bihaqo/TT-MRF (footnote 3 on page 5) |
| Open Datasets | Yes | In our evaluation we mainly use the homogeneous and heterogeneous Ising models, and we refer to the supplementary material for the detailed description of the experimental setup. ... First we compare our method (TT) against the following methods implemented in the Lib DAI system (Mooij, 2010): ... |
| Dataset Splits | No | The paper describes generating models and evaluating them, but does not specify clear train/validation/test dataset splits (e.g., percentages, sample counts, or citations to predefined splits) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper states that experiments were run using a MATLAB implementation and provides running times, but no specific hardware details (such as CPU/GPU models or memory specifications) are mentioned. |
| Software Dependencies | Yes | In all our experiments we use a non-optimized MATLAB implementation3 of our methods. For operations related to the TT-format we use the TT-Toolbox4 implemented in MATLAB as well. ... 4http://spring.inm.ras.ru/osel/download/tt22.zip |
| Experiment Setup | Yes | For AIS method we select parameters (1000 intermediate distributions, 70 samples each) to maximize the accuracy achieved within 60 seconds per model. ... For each value of temperature T we generate 50 homogeneous 10x10 Ising models... Here pairwise weights are generated uniformly from [f, f] with parameter f varying from 0.25 to 3. |