Message Passing for Collective Graphical Models
Authors: Tao Sun, Dan Sheldon, Akshat Kumar
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate NLBP with two sets of experiments. First, we evaluate the extent to which NLBP accelerates CGM inference and learning for a benchmark synthetic bird migration problem (Sheldon et al., 2013; Liu et al., 2014). Then, we demonstrate the benefits of a more scalable inference algorithm by evaluating CGMs in a new application: learning with noisy sufficient statistics. |
| Researcher Affiliation | Academia | 1University of Massachusetts Amherst, 2Mount Holyoke College, 3Singapore Management University |
| Pseudocode | Yes | Algorithm 1: Non-Linear Belief Propagation |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code related to the methodology described. |
| Open Datasets | No | Synthetic data is generated from a chain-structured CGM to simulate migration of a population of M birds from the bottomleft corner to the top-right corner of an ℓ ℓgrid. |
| Dataset Splits | No | The paper generates synthetic data and simulates trajectories rather than explicitly defining training, validation, and test splits from a pre-existing dataset. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'MATLAB's interior-point algorithm' but does not specify the version numbers for MATLAB or any other software dependencies. |
| Experiment Setup | Yes | In the following experiments, we set M =1000, T =20 and vary grid size L from 5 5 to 19 19. We report results for wtrue =(5, 10, 10, 10). we added Poisson noise y Pois(αn) to the nodes, with detection rate α=1. For the CGM-based algorithms, we ran 250 EM iterations, which was enough for convergence in almost all cases. |