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..
Adaptive Belief Propagation
Authors: Georgios Papachristoudis, John Fisher
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show in synthetic and real experiments that it outperforms standard BP by orders of magnitude and explore the settings that it is advantageous over (S umer et al., 2011). |
| Researcher Affiliation | Academia | Georgios Papachristoudis EMAIL John W. Fisher III EMAIL CSAIL, MIT, Cambridge, MA 02139, USA |
| Pseudocode | Yes | Algorithm 1 ADAPTIVE BP |
| Open Source Code | Yes | An implementation of this algorithm is publicly available at https://github.com/geopapa11/adabp |
| Open Datasets | Yes | Specifically, we explore the effects of pointwise mutations in DNA sequences to the birth or disappearance of Cp G islands...obtained from the NCBI database. We analyzed temperature measurements collected from 53 wireless sensors at 30 sec intervals from the Intel Berkeley Research lab. |
| Dataset Splits | No | The paper mentions training models and using various datasets, but it does not specify explicit train/validation/test split percentages or sample counts for reproducibility in the main text. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions tools and other methods used (e.g., 'Cp G Island Searcher', 'RCTree BP') but does not specify software dependencies with version numbers (e.g., Python, PyTorch versions) for reproducibility. |
| Experiment Setup | No | The paper describes general experimental procedures and data usage (e.g., 'randomly generate different w orders', 'train the parameters of HMM') but does not provide specific hyperparameters or system-level training settings like learning rates, batch sizes, or optimizer details in the main text. |