Decentralized Approximate Bayesian Inference for Distributed Sensor Network
Authors: Behnam Gholami, Sejong Yoon, Vladimir Pavlovic
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we first 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 {bb510, sjyoon, vladimir}@cs.rutgers.edu |
| 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 η = 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). |