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..
On Symmetric and Asymmetric LSHs for Inner Product Search
Authors: Behnam Neyshabur, Nathan Srebro
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also compared the hash functions empirically, following the exact same protocol as Shrivastava and Li (2014a), using two collaborative filtering datasets, Netflix and Movielens 10M. |
| Researcher Affiliation | Academia | Toyota Technological Institute at Chicago, Chicago, IL 60637, USA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is openly available. |
| Open Datasets | Yes | We also compared the hash functions empirically, following the exact same protocol as Shrivastava and Li (2014a), using two collaborative filtering datasets, Netflix and Movielens 10M. |
| Dataset Splits | No | The paper describes how the data is used for queries and database points in the collaborative filtering setup, but does not provide specific train/validation/test dataset splits (e.g., percentages or sample counts) for the overall datasets. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For L2-ALSH(SL) we used m = 3, U = 0.83, r = 2.5, suggested by the authors and used in their empirical evaluation. For SIGN-ALSH(SL) we used two different settings of the parameters suggested by Shrivastava and Li (2014b): m = 2, U = 0.75 and m = 3, U = 0.85. SIMPLE-LSH does not require any parameters. |