Extending the Modelling Capacity of Gaussian Conditional Random Fields while Learning Faster
Authors: Jesse Glass, Mohamed Ghalwash, Milan Vukicevic, Zoran Obradovic
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Benefits of the proposed model in terms of improved accuracy and speed are characterized on several synthetic graphs with 2 million links as well as on a hospital admissions prediction task represented as a human disease-symptom similarity network corresponding to more than 35 million hospitalization records in California over 9 years. |
| Researcher Affiliation | Academia | Jesse Glass Temple University Philadelphia, USA tud25892@temple.edu Mohamed Ghalwash Temple University Philadelphia, USA tuc30491@temple.edu Milan Vukicevic University of Belgrade Belgrade, Serbia vukicevicm@fon.bg.ac.rs Zoran Obradovic Temple University Philadelphia, USA zobrad@gmail.com |
| Pseudocode | No | The paper describes the mathematical formulation and optimization steps but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | That table is publicly available at http://astro.temple.edu/ tud25892. |
| Open Datasets | Yes | We evaluated Um GCRF on the problem of predicting monthly hospital admissions for 189 classes of diseases in California from HCUP data (HCUP 2011). HCUP. 2011. HCUP State Inpatient Databases (SID). Healthcare Cost and Utilization Project (HCUP). 2005-2009. Agency for Healthcare Research and Quality, Rockville, MD. http://www.hcupus.ahrq.gov//sidoverview.jsp. |
| Dataset Splits | No | We train on the first 80 months and test on the remaining 27. |
| Hardware Specification | No | The following speed tests were done in Matlab with a single feature per target variable. |
| Software Dependencies | No | The following speed tests were done in Matlab with a single feature per target variable. |
| Experiment Setup | Yes | NN had 26 hidden nodes. The algorithm was tested 100 times because the NN is non-convex and yields different results each time. |