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..
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. |