Dimensionality Reduction for General KDE Mode Finding
Authors: Xinyu Luo, Christopher Musco, Cas Widdershoven
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We support our theoretical results with experiments on two datasets, MNIST (60000 data points, 784 dimensions) and Text8 (71290 data points, 300 dimensions). |
| Researcher Affiliation | Academia | 1Department of Computer Science, Purdue University, Indiana, USA 2Tandon School of Engineering, New York University, New York, USA 3State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China. |
| Pseudocode | Yes | Algorithm 1 Mean-Shift Algorithm; Algorithm 2 Mode Recovery for Convex Kernels |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., links or explicit statements of release) for source code. |
| Open Datasets | Yes | We support our theoretical results with experiments on two datasets, MNIST (60000 data points, 784 dimensions) and Text8 (71290 data points, 300 dimensions). |
| Dataset Splits | No | The paper mentions using MNIST and Text8 datasets but does not specify any training/validation/test splits, percentages, or the methodology for partitioning the data. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | we obtain a baseline mode value by running the mean-shift heuristic (gradient descent) for 100 iterations, with 60 randomly chosen starting points. We ran for 10 iterations with 30 random restarts, chosen as described above. |