Scaling-Up MAP and Marginal MAP Inference in Markov Logic
Authors: Somdeb Sarkhel
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on both synthetic and real-world MLNs demonstrated the scalability of our approach. Experimental results show that our approach is superior to existing approaches in terms of scalability and accuracy. We demonstrate experimentally that our method is highly scalable and yields close to optimal solutions in a fraction of the time as compared to existing approaches. Our experiments on large datasets demonstrate that our approach is both accurate and scalable compared to state-of-the-art MLN systems like Alchemy and Tuffy. |
| Researcher Affiliation | Academia | Somdeb Sarkhel Department of Computer Science The University of Texas at Dallas somdeb.sarkhel@utdallas.edu |
| Pseudocode | No | The paper describes algorithms and approaches verbally but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to open-source code for the methodologies described, nor does it explicitly state that the code will be made available. |
| Open Datasets | No | The paper mentions using "synthetic and real-world MLNs" and "large datasets" for experiments, but it does not provide concrete access information (e.g., specific names, links, DOIs, or proper citations) for any publicly available datasets. |
| Dataset Splits | No | The paper does not specify any dataset splits for training, validation, or testing (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., CPU, GPU models, memory specifications). |
| Software Dependencies | No | The paper mentions using "existing solvers" and refers to "off-the-shelf ILP solver" and MLN systems like "Alchemy and Tuffy," but it does not provide specific version numbers for any software dependencies, which is required for reproducibility. |
| Experiment Setup | No | The paper describes the methods and general experimental findings but does not provide specific details about the experimental setup, such as hyperparameters, optimization settings, or training configurations. |