The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings
Authors: Krzysztof M. Choromanski, Mark Rowland, Adrian Weller
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide geometric and Markov chain-based perspectives to help understand the beneļ¬ts, and empirical results which suggest that the approach is helpful in a wider range of applications. In 6 we provide empirical results which support our analysis, and show that ROMs are effective for a still broader set of applications. |
| Researcher Affiliation | Collaboration | Krzysztof Choromanski Google Brain Robotics kchoro@google.com Mark Rowland University of Cambridge mr504@cam.ac.uk Adrian Weller University of Cambridge and Alan Turing Institute aw665@cam.ac.uk |
| Pseudocode | No | No structured pseudocode or algorithm blocks were explicitly presented in the main body of the paper. While Lemma 3.5 refers to an algorithm in the Appendix, the main text does not include or link to its pseudocode directly. |
| Open Source Code | No | No explicit statement about providing open-source code or a link to a code repository was found in the paper. |
| Open Datasets | No | No concrete access information (specific link, DOI, repository name, formal citation with authors/year) for the mentioned datasets (g50c, LETTER, USPS) was provided, nor were they explicitly stated as publicly available with access details. |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology for train/validation/test) was explicitly provided in the paper. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running the experiments were provided in the paper. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiments were provided in the paper. |
| Experiment Setup | No | No specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings were provided in the main text of the paper. |