Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Decentralized Approximate Bayesian Inference for Distributed Sensor Network
Authors: Behnam Gholami, Sejong Yoon, Vladimir Pavlovic
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we ο¬rst demonstrate the general convergence properties of the D-BPCA algorithm on synthetic data. We then apply our model to a set of Structure from Motion (Sf M) problems. We compared our distributed algorithm with traditional SVD, Centralized PCA (PPCA) (Tipping and Bishop 1999), Distributed PPCA (D-PPCA) (Yoon and Pavlovic 2012), and Centralized BPCA (BPCA) (Ilin and Raiko 2010). |
| Researcher Affiliation | Academia | Behnam Gholami, Sejong Yoon and Vladimir Pavlovic Rutgers, The State University of New Jersey 110 Frelinghuysen Road Piscataway, NJ 08854-8019 EMAIL |
| Pseudocode | No | The paper describes algorithms and derivations through mathematical equations and text, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We applied our model to the Caltech 3D Objects on Turntable dataset (Moreels and Perona 2007) and Hopkins155 dataset (Tron and Vidal 2007) |
| Dataset Splits | No | The paper mentions partitioning frames for distributed simulation and describes data usage for online learning, but it does not specify explicit training/validation/test dataset splits (e.g., percentages or sample counts) needed for reproduction. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory, or cloud computing specifications used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with versions) that were used to conduct the experiments. |
| Experiment Setup | Yes | For all Sf M experiments, the network has the ring topology, with Ξ· = 10. We equally partitioned the frames into 5 nodes to simulate 5 cameras, the convergence was set to 10^-3 relative change in objective of (15). |