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].
On the Identifiability of Nonlinear ICA: Sparsity and Beyond
Authors: Yujia Zheng, Ignavier Ng, Kun Zhang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments To validate the proposed theory of the identifiability of nonlinear ICA with unconditional priors, we conduct experiments based on our main condition, i.e., structural sparsity (Thm. 1), as well as the condition of the independent influences (Prop. 2). |
| Researcher Affiliation | Academia | 1 Carnegie Mellon University 2 Mohamed bin Zayed University of Artificial Intelligence EMAIL |
| Pseudocode | No | The paper contains mathematical formulations and theorems but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statements about releasing source code or providing a link to a code repository for the described methodology. |
| Open Datasets | Yes | To study how reasonable the proposed theories are w.r.t. the practical generating process of observational data, we conduct experiments on the Triangles' image dataset (Yang et al., 2022). |
| Dataset Splits | No | The paper mentions training models and conducting an ablation study but does not explicitly provide details about training, validation, and test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, or cloud computing instance types, used for conducting the experiments. |
| Software Dependencies | No | The paper refers to using a 'GIN' model for training and specific norms (L1, L0) but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch versions). |
| Experiment Setup | No | The paper mentions aspects of the experimental setup like the objective function and regularization terms, but it does not provide specific details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings. |