Making Pairwise Binary Graphical Models Attractive

Authors: Nicholas Ruozzi, Tony Jebara

NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We illustrate the theory through a series of experiments on small models, grid graphs, and vertex induced subgraphs of the Epinions social network
Researcher Affiliation Academia Nicholas Ruozzi Institute for Data Sciences and Engineering Columbia University New York, NY 10027 nr2493@columbia.edu Tony Jebara Department of Computer Science Columbia University New York, NY 10027 jebara@cs.columbia.edu
Pseudocode No The paper does not contain structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide any statement or link for open-source code availability.
Open Datasets No The paper mentions the 'Epinions social network' in a footnote but does not provide specific access information (link, DOI, or formal citation with authors/year) for the dataset itself.
Dataset Splits No The paper does not provide specific dataset split information (exact percentages, sample counts, or citations to predefined splits) needed for data partitioning.
Hardware Specification No The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory) used for running experiments.
Software Dependencies No The paper mentions a 'standard implementation of reweighted, asynchronous message passing' but does not provide specific software dependencies with version numbers.
Experiment Setup Yes We test the performance of these algorithms on Ising models with a randomly selected external field and various interaction strengths on the edges. ... The algorithms were run until the messages in consecutive time steps differed by less than 10 8 or until more than 20, 000 iterations were performed (a single iteration consists of updating all of the messages in the model).