Quadrature-based features for kernel approximation
Authors: Marina Munkhoeva, Yermek Kapushev, Evgeny Burnaev, Ivan Oseledets
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We derive the convergence behaviour and conduct an extensive empirical study that supports our hypothesis |
| Researcher Affiliation | Academia | Skolkovo Institute of Science and Technology Moscow, Russia Institute of Numerical Mathematics of the Russian Academy of Sciences Moscow, Russia |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | Yes | The code for this paper is available at https://github.com/maremun/quffka. |
| Open Datasets | Yes | We extensively study the proposed method on several established benchmarking datasets: Powerplant, LETTER, USPS, MNIST, CIFAR100 [23], LEUKEMIA [20]. |
| Dataset Splits | No | The paper provides dataset names and overall sizes in Table 2, but it does not specify explicit train/validation/test splits (e.g., percentages, sample counts, or references to predefined splits) needed for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Approximation was constructed for different number of SR samples n = D 2(d+1)+1, where d is an original feature space dimensionality and D is the new one. For the Gaussian kernel we set hyperparameter γ = 1 2σ2 to the default value of 1 d for all the approximants. |