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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |