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..
Compact Random Feature Maps
Authors: Raffay Hamid, Ying Xiao, Alex Gittens, Dennis Decoste
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experiments on multiple standard data-sets with performance competitive with state-of-the-art results. We now present CRAFTMaps results on multiple data-sets. |
| Researcher Affiliation | Collaboration | Raffay Hamid EMAIL e Bay Research Laboratory Ying Xiao EMAIL Georgia Institute of Technology Alex Gittens EMAIL e Bay Research Laboratory Dennis De Coste EMAIL e Bay Research Laboratory |
| Pseudocode | Yes | Algorithm 1 RANDOM FEATURE MAPS (RFM) Algorithm 2 CRAFTMAPS USING RFM |
| Open Source Code | No | The paper does not contain any statements about releasing source code or provide links to a code repository. |
| Open Datasets | Yes | Figure 5 shows the normalized root mean square errors (NRMSE) for MNIST data obtained for an r = 7 and q = 1 kernel using random feature maps... Figure 6 where NRMSE for 6 different data-sets over a range of E are shown. Table 1 shows the test classification error using CRAFTMaps on random feature maps (Kar & Karnick, 2012) and tensor sketch (Pham & Pagh, 2013) compared to 5 alternate approaches over 4 different data-sets. (These datasets are MNIST, USPS, COIL100, PENDIGITS) Results on MNIST8M Data Figure 7 (b) shows the comparative performance of CRAFTMaps for a given sized E (2^14) as training size varies from 60K to 8.1M. |
| Dataset Splits | No | The paper mentions 'training data' and 'test points' and uses 'randomly selected' or 'randomly picked' points, but it does not specify explicit train/validation/test splits, percentages, or absolute counts for validation data. |
| Hardware Specification | No | The paper mentions 'These times were recorded for MNIST data using a 40-core machine.' This provides a general core count but lacks specific CPU models, GPU details, or other detailed hardware specifications. |
| Software Dependencies | No | The paper mentions 'BLAS3' in relation to exploiting multi-core processing power, but it does not list any specific software libraries or their version numbers. |
| Experiment Setup | Yes | Unless otherwise mentioned, we use the H-0/1 heuristic of (Kar & Karnick, 2012) for random feature maps (RFM) and CRAFTMaps on RFM. These results were obtained for an r = 7 and q = 1 kernel. Here r = 7, 7, 5, 5 and 9 respectively while q = 1. These results were obtained using a polynomial kernel with r = 7, q = 1, D = 2^17, E = 2^14, and ECOCs equal to 200. |