Adaptive Belief Propagation
Authors: Georgios Papachristoudis, John Fisher
ICML 2015 | Conference PDF | Archive PDF | Plain Text | 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 GEOPAPA@MIT.EDU John W. Fisher III FISHER@CSAIL.MIT.EDU 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. |