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].
Manifold-valued Dirichlet Processes
Authors: Hyunwoo Kim, Jia Xu, Baba Vemuri, Vikas Singh
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments To evaluate the proposed model, we conduct a set of experiments on synthetic and real-world data. |
| Researcher Affiliation | Academia | University of Wisconsin-Madison, Madison, WI 53706, USA University of Florida, Gainesville, FL 32611, USA |
| Pseudocode | Yes | Algorithm 1 HMC algorithm for DP-MGLM on Riemannian manifolds |
| Open Source Code | No | The paper provides a project page URL (http://pages.cs.wisc.edu/~hwkim/projects/dp-mglm/) but does not explicitly state that source code is provided there, nor is it a direct link to a code repository like GitHub. |
| Open Datasets | Yes | We used the Lifespan database (Minear & Park, 2004), which contains 580 subjects with ages ranging from 18 93. |
| Dataset Splits | No | The paper mentions 'Train' and 'Test' in Table 1 and sample sizes ('Our sample size is 300') but does not specify the explicit percentages or counts for training, validation, and testing splits. |
| Hardware Specification | No | The paper does not specify any particular hardware used for running the experiments (e.g., specific CPU or GPU models, or cloud computing environments). |
| Software Dependencies | No | The paper does not mention any specific software or library names with version numbers (e.g., Python, PyTorch, TensorFlow, etc.) that were used. |
| Experiment Setup | Yes | We perform a few hundred realizations where the number of MCMC samples in each realization is 1000. We set the burn-in period to 100 epochs. |