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..
On the Model Shrinkage Effect of Gamma Process Edge Partition Models
Authors: Iku Ohama, Issei Sato, Takuya Kida, Hiroki Arimura
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally confirmed that the model shrinkage of the proposed models works well and that the IDEPM indicated state-of-the-art performance in generalization ability, link prediction accuracy, mixing efficiency, and convergence speed. |
| Researcher Affiliation | Collaboration | Iku Ohama Panasonic Corp., Japan Issei Sato The Univ. of Tokyo, Japan Takuya Kida Hiroki Arimura Hokkaido Univ., Japan |
| Pseudocode | Yes | Sampling z: Given m, similar to the Chinese restaurant process (CRP) [15], the posterior probability that zi,j,s is assigned to k is given as follows: P(zi,j,s = k | z\(ijs), m) ... Sampling m: Given z, posteriors for the φ and ψ are simulated as ... Therefore, similar to the sampler for the EPM [8], we can update m as follows: mi,j, | φ, ψ, λ δ(0) if xi,j = 0, ZTP(PK+ k=1 φi,kψj,kλk) if xi,j = 1, {mi,j,k}K+ k=1 | mi,j, , φ, ψ, λ Multinomial |
| Open Source Code | No | The paper does not provide concrete access to source code or explicitly state its release. |
| Open Datasets | Yes | The first dataset was the Enron [16] dataset, which comprises e-mails sent between 149 Enron employees. ... For larger dataset, we used the Movie Lens [17] dataset, which comprises five-point scale ratings of movies submitted by users. |
| Dataset Splits | Yes | Furthermore, all reported measurements were averaged values obtained by 10-fold cross validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We ran 600 Gibbs iterations for each model on each dataset and used the final 100 iterations to calculate the measurements. Furthermore, all reported measurements were averaged values obtained by 10-fold cross validation. ... For the purpose of fair comparison, we set hyper-hyperparameters as e0 = f0 = 0.01 throughout the experiments. |