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].
Spherical Structured Feature Maps for Kernel Approximation
Authors: Yueming Lyu
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, SSF maps achieve superior performance compared with other methods. |
| Researcher Affiliation | Academia | 1Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Hong Kong . |
| Pseudocode | Yes | Algorithm 1 |
| Open Source Code | No | The paper does not provide explicit statements or links to open-source code for the methodology described. |
| Open Datasets | Yes | We evaluate reconstruction error of Gaussian kernel, zeroorder arc-cosine kernel and ο¬rst-order arc-cosine kernel on CIFAR10 (Krizhevsky & Hinton, 2009), MNIST (Le Cun & Cortes, 2010), usps and dna dataset. |
| Dataset Splits | No | The paper mentions randomly selecting 2,000 samples for constructing the Gram matrix but does not specify clear training, validation, or test dataset splits or percentages. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments are provided. |
| Software Dependencies | No | The paper mentions using 'MATLAB' but does not specify any version numbers for MATLAB or any specific software dependencies. |
| Experiment Setup | Yes | In all the experiments, we ο¬x M = 1 (the number of one-dimensional QMC points) for SSF maps. |