New Liftable Classes for First-Order Probabilistic Inference
Authors: Seyed Mehran Kazemi, Angelika Kimmig, Guy Van den Broeck, David Poole
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In order to see the effect of using domain recursion in practice, we find the WFOMC of three theories with and without using the domain recursion rule: (a) the theory in Example 3, (b) the S4 clause, and (c) the symmetric-transitivity theory. We implemented the domain recursion rule in C++ and compiled the codes using the g++ compiler. We compare our results with the WFOMC-v3.0 software5. Since this software requires domain-liftable input theories, for the first theory we grounded the jobs, for the second we grounded x, and for the third we grounded p. For each of these three theories, assuming | x| = n for all LVs x in the theory, we varied n and plotted the run-time as a function of n. All experiments were done on a 2.8GH core with 4GB RAM under Mac OSX. The run-times are reported in seconds. We allowed a maximum of 1000 seconds for each run. Obtained results can be viewed in Fig. 1. |
| Researcher Affiliation | Academia | Seyed Mehran Kazemi The University of British Columbia smkazemi@cs.ubc.ca Angelika Kimmig KU Leuven angelika.kimmig@cs.kuleuven.be Guy Van den Broeck University of California, Los Angeles guyvdb@cs.ucla.edu David Poole The University of British Columbia poole@cs.ubc.ca |
| Pseudocode | No | The paper describes rules and processes but does not include any formal pseudocode blocks or algorithms. |
| Open Source Code | No | The paper states, 'We implemented the domain recursion rule in C++' but does not provide a link or explicit statement that their implementation is open-source. It does link to WFOMC-v3.0 software, but this is a tool they used, not their own open-source code for their method. |
| Open Datasets | No | The paper conducts experiments on theoretical models (e.g., S4 clause, symmetric-transitivity theory) by varying population size, which are not datasets in the typical sense of needing public access information. |
| Dataset Splits | No | The paper does not describe dataset splits for training, validation, or testing, as it focuses on evaluating algorithmic performance on theoretical problem instances rather than training machine learning models on empirical datasets. |
| Hardware Specification | Yes | All experiments were done on a 2.8GH core with 4GB RAM under Mac OSX. |
| Software Dependencies | Yes | We implemented the domain recursion rule in C++ and compiled the codes using the g++ compiler. We compare our results with the WFOMC-v3.0 software5. 5Available at: https://dtai.cs.kuleuven.be/software/wfomc |
| Experiment Setup | Yes | For each of these three theories, assuming | x| = n for all LVs x in the theory, we varied n and plotted the run-time as a function of n. All experiments were done on a 2.8GH core with 4GB RAM under Mac OSX. The run-times are reported in seconds. We allowed a maximum of 1000 seconds for each run. |