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 [1].
Discovering Latent Network Structure in Point Process Data
Authors: Scott Linderman, Ryan Adams
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate this new model empirically on several datasets. |
| Researcher Affiliation | Academia | Scott W. Linderman EMAIL Harvard University, Cambridge, MA 02138 USA Ryan P. Adams EMAIL Harvard University, Cambridge, MA 02138 USA |
| Pseudocode | No | The paper describes the inference procedure using text and mathematical equations but does not include structured pseudocode or an algorithm block. |
| Open Source Code | Yes | We have implemented our inference algorithm on GPUs to capitalize on this parallelism. 1https://github.com/slinderman |
| Open Datasets | Yes | We study gang-related homicides between 1980 and 1995 (Block et al., 2005). |
| Dataset Splits | Yes | We evaluate our model with an event-prediction task, training on 1980-1993 and testing on 1994-1995. |
| Hardware Specification | No | The paper mentions 'implemented our inference algorithm on GPUs' but does not provide specific details such as GPU model numbers, CPU specifications, or memory details used for the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, such as programming languages, libraries, or solvers. |
| Experiment Setup | Yes | We look for short-term interactions on top of this background rate with time scales of tmax = 60s. Figure 6 shows a sample from the posterior distribution over embeddings in R2 for ρ = 0.2 and τ = 1. |