Inference and Sampling of $K_33$-free Ising Models
Authors: Valerii Likhosherstov, Yury Maximov, Misha Chertkov
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To test the correctness of inference, we generate random K33-free models of a given size and then compare the value of PF computed in a brute force way (tractable for sufficiently small graphs) and by our algorithm. We simulate samples of sizes from {10, ..., 15} (1000 samples per size) and verify that respective expressions coincide. [...] Finally, we simulate inference and sampling for random models of different size N and observe that the computational time (efforts) scales as O(N 3 2 ) (Fig. 6)1. |
| Researcher Affiliation | Academia | 1Skolkovo Institute of Science and Technology, Moscow, Russia 2Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA 3Graduate Program in Applied Mathematics, University of Arizona, Tucson, AZ, USA. |
| Pseudocode | No | The paper describes algorithms in text but does not include any clearly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Implementation of the algorithms is available at https://github.com/Valery Tyumen/planar ising. |
| Open Datasets | No | The paper states: 'To test the correctness of inference, we generate random K33-free models of a given size...' rather than using a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper does not describe specific training, validation, or test dataset splits. It mentions generating 'random K33-free models' and simulating samples. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or detailed computer specifications) used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as hyperparameter values, optimizer settings, or detailed training configurations. |