Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Navigating the Effect of Parametrization for Dimensionality Reduction
Authors: Haiyang Huang, Yingfan Wang, Cynthia Rudin
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 29,739 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,926 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments Here, we evaluate the performance of our Param Pa CMAP and Param Respulsor algorithms empirically. To contextualize our findings, we juxtapose our results against those obtained from other contemporary parametric DR algorithms. Visualization for the embeddings generated by all algorithms can be found in App. C. |
| Researcher Affiliation | Academia | Haiyang Huang Yingfan Wang Cynthia Rudin Duke University EMAIL |
| Pseudocode | Yes | Pseudocode for Param Repulsor is found in Alg. 1 and detailed in Alg. 2 in App. F. |
| Open Source Code | Yes | Our code is available at https://github.com/hyhuang00/Param Repulsor. |
| Open Datasets | Yes | For image analysis, we analyzed the MNIST [15] and Fashion-MNIST (F-MNIST) [32] datasets, along with COIL-20 [33] and COIL-100 [34]. |
| Dataset Splits | Yes | We perform leave-one-out cross validation, and utilize a k-NN classifier to predict the label of the point. |
| Hardware Specification | Yes | All experiments are conducted with an Exxact Tensor EX 2U Server with 2 Intel Xeon Ice Lake Gold 5317 Processors @ 3.0GHz. We limit the RAM usage to be 32GB. Parallel computation are performed over a single Nvidia RTX A5000 GPU. |
| Software Dependencies | Yes | Param Repulsor and Param Pa CMAP are implemented with Py Torch 2.0.0, Numba 0.57.0 and CUDA 11.7. |
| Experiment Setup | Yes | Unless otherwise specified, we utilize a network of three hidden layers with [100, 100, 100] neurons. Param Repulsor utilizes Si LU as the activation function, whereas Param Pa CMAP utilizes Re LU just as the other methods. |