Probabilistic Variational Bounds for Graphical Models
Authors: Qiang Liu, John W. Fisher III, Alexander T. Ihler
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments We demonstrate our algorithm using synthetic Ising models, and real-world models from recent UAI inference challenges. |
| Researcher Affiliation | Academia | Qiang Liu Computer Science Dartmouth College qliu@cs.dartmouth.edu John Fisher III CSAIL MIT fisher@csail.mit.edu Alexander Ihler Computer Science Univ. of California, Irvine ihler@ics.uci.edu |
| Pseudocode | No | The paper describes algorithms verbally and mathematically, but does not contain a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing source code for the work described, nor does it provide a direct link to a code repository. |
| Open Datasets | No | The paper mentions 'real-world models from recent UAI inference challenges' (e.g., BN 6, BN 11, pedigree20) which are established benchmark instances, but it does not provide specific links, DOIs, repository names, or formal citations with authors and year for accessing these datasets directly. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or test sets. The experiments are conducted on synthetic and UAI challenge instances, which are typically used whole for inference. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | We start with a simple Ising model with θk(xk) = σsxk and θkl(xk, xl) = σpxkxl, xk { 1, 1}, where σs represents the external field and σp the correlation. We fix σs = 0.01 and vary σp from 1.5 (strong negative correlation) to 1.5 (strong positive correlation). ... Figure 4 shows the results for ibound 8 and 15. |