Lifted Inference with Linear Order Axiom
Authors: Jan Tóth, Ondřej Kuželka
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To check our results empirically, as well as to assess how our approach scales, we implemented the proposed algorithm in the Julia programming language (Bezanson et al. 2017). The implementation follows the algorithmic approach presented in the paper, with one notable exception. Counting quantifiers and cardinality constraints are not handled by repeated calls to a WFOMC oracle and subsequent polynomial interpolation (Kuˇzelka 2021). Instead, they are processed by introducing a symbolic variable3 for each cardinality constraint and computing the polynomial (that would be interpolated) explicitly in a single run of the algorithm. We made use of the Nemo.jl package (Fieker et al. 2017) for polynomial representation and manipulation. Figure 1 depicts the inference results for a domain size n = 10 and weight w = 3. |
| Researcher Affiliation | Academia | Jan T oth, Ondˇrej Kuˇzelka Faculty of Electrical Engineering Czech Technical University in Prague Prague, Czech Republic {tothjan2, ondrej.kuzelka}@fel.cvut.cz |
| Pseudocode | Yes | Algorithm 1 Incremental WFOMC |
| Open Source Code | No | The paper does not provide a direct link or explicit statement that its own implementation code is open-source. It mentions using the Nemo.jl package, which is a third-party tool. |
| Open Datasets | No | The paper describes a custom graph model for its experiments ('Our Model') and references the Watts-Strogatz model, but it does not specify a publicly available dataset used for training or provide access information for one. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits. The experiments are conducted on a generated graph model with specific parameters. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper states 'we implemented the proposed algorithm in the Julia programming language' and 'We made use of the Nemo.jl package'. While it names the software, it does not provide specific version numbers for Julia or Nemo.jl. |
| Experiment Setup | Yes | Figure 1 depicts the inference results for a domain size n = 10 and weight w = 3. The parameter m is set to 5, 8 and 10, respectively. |