Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs
Authors: Mayukh Das, Devendra Singh Dhami, Gautam Kunapuli, Kristian Kersting, Sriraam Natarajan7816-7824
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results demonstrate the efficiency of these approximations, which can be applied to many complex statistical relational models, and can be significantly faster than state-of-the-art, both for inference and learning, without sacrificing effectiveness. We investigate the following questions: (Q1) Is MACH effective and efficient in full model learning with n-ary relations compared to a robust baseline? (Q2) Is modeling n-ary relations faithfully crucial when learning relational model? and (Q3) How does MACH compare (scaling vs. performance) to a state-of-the-art database-centric MLN system? ... Table 1 summarizes the performance and efficiency results of MACH against the baselines for structure and parameter learning of MLNs. |
| Researcher Affiliation | Academia | Mayukh Das University of Texas, Dallas mayukh.das1@utdallas.edu Devendra Singh Dhami University of Texas, Dallas devendra.dhami@utdallas.edu Gautam Kunapuli University of Texas, Dallas gautam.kunapuli@utdallas.edu Kristian Kersting Technical University of Darmstadt kersting@cs.tu-darmstadt.de Sriraam Natarajan University of Texas, Dallas sriraam.natarajan@utdallas.edu |
| Pseudocode | Yes | Algorithm 1 MACH: Motif-based Approximate Counting via Hypergraphs |
| Open Source Code | Yes | 3Code @ https://github.com/mayukhdas/MACH |
| Open Datasets | Yes | We used three standard SRL data sets: UW-CSE, Citeseer and Web KB, a biomedical data set Carcinogenesis (Srinivasan et al. 1997), and an NLP/Information Extraction(IE) data set NELL-Sports for evaluation. |
| Dataset Splits | No | We computed AUC-ROC, AUC-PR, CLL, F1 and running times averaged over 5 random train/test splits. The paper does not explicitly mention validation dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions 'Java-based Hypergraph DB architecture' but does not provide specific version numbers for Java or Hypergraph DB, or any other software dependencies. |
| Experiment Setup | No | The paper describes the system and baselines but does not provide specific experimental setup details such as hyperparameter values, model initialization, or training schedules. |