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].
Revisiting Gaussian Process Dynamical Models
Authors: Jing Zhao, Shiliang Sun
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on incomplete motion capture data (walk, run, swing and multiple-walker) and make comparisons with the existing four algorithms as well as k NN, spline interpolation and VGPDS. Our methods perform much better on both training with incomplete data and recovering incomplete test data. |
| Researcher Affiliation | Academia | Jing Zhao, Shiliang Sun Shanghai Key Laboratory of Multidimensional Information Processing, Department of Computer Science and Technology, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China |
| Pseudocode | Yes | Algorithm 1 MAP+ estimation of {X, α, β, W}. and Algorithm 2 T.MAP+ estimation of {X, α, β, W}. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for their methodology or a link to a repository. |
| Open Datasets | Yes | The benchmark data used for experiments are human motion capture data from the Carnegie Mellon University motion capture database. |
| Dataset Splits | No | The paper mentions 'training with incomplete data' and 'recovering incomplete test data' but does not provide explicit training/validation/test dataset splits (percentages, sample counts, or citations to predefined splits) needed for reproduction. |
| Hardware Specification | No | The paper does not specify any hardware details such as CPU or GPU models, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We set d = 3, I = 100 and J = 10 in our experiments. We set d = 3, R = 50, I = 10, J = 10 and K = 10 in our experiments. |