Lifted Hybrid Variational Inference
Authors: Yuqiao Chen, Yibo Yang, Sriraam Natarajan, Nicholas Ruozzi
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We investigate the performance of the proposed lifted variational inference approach on a variety of both real and synthetic models. We compare the performance of our variational approach using different entropy approximations...with message-passing algorithms... |
| Researcher Affiliation | Academia | Yuqiao Chen 1 , Yibo Yang 2 , Sriraam Natarajan1 and Nicholas Ruozzi1 1 The University of Texas at Dallas 2 University of California Irvine |
| Pseudocode | Yes | Algorithm 1 Coarse-to-Fine Lifted VI |
| Open Source Code | Yes | Source code is available on https://github.com/leodd/Lifted-Hybrid-Variational-Inference. |
| Open Datasets | Yes | We construct a Toy Hybrid MLN for a position domain: The Paper Popularity HMLN domain is determined by the following formulas. The Robot Mapping HMLN domain contains 3 discrete relational variables... We performed approximate inference on a RGM with the recession domain from [Cseke and Heskes, 2011]. We use extracted groundwater level data from the Republican River Compact Association model [Mc Kusick, 2003]. |
| Dataset Splits | No | The paper describes generating random evidence for models and randomly choosing variables for testing, but it does not specify any explicit train/validation/test dataset splits, percentages, or cross-validation methods. |
| Hardware Specification | Yes | All timing results, unless otherwise noted, were performed on a single core of a 2.2 GHz Intel Core i7-8750H CPU with 16GB memory. |
| Software Dependencies | No | The paper mentions the use of 'Tensorflow' but does not specify its version number or the versions of any other software dependencies, making replication difficult. |
| Experiment Setup | Yes | All three methods used the Adam optimizer with learning rate 0.2, β1 = 0.9, β2 = 0.999. |