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..
Learning a Metric Embedding for Face Recognition using the Multibatch Method
Authors: Oren Tadmor, Tal Rosenwein, Shai Shalev-Shwartz, Yonatan Wexler, Amnon Shashua
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | A Appendix: Proof of Theorem 1 We ο¬rst show that the estimate is unbiased... This concludes our proof. Lemma 1 Let v 2 Rn be any vector. Then, E s6=t[vsvt] (E In particular, if Ei[vi] = 0 then P s6=t vsvt 0. Proof For simplicity, we use E[v] for Ei[vi] and E[v2] for Ei[v2 |
| Researcher Affiliation | Academia | The provided text is an appendix and does not contain author affiliations or contact information. |
| Pseudocode | No | The provided text is an appendix containing mathematical proofs and does not include pseudocode or algorithm blocks. |
| Open Source Code | No | The provided text is an appendix focusing on a mathematical proof and does not mention the release of open-source code for the methodology. |
| Open Datasets | No | The provided text is an appendix focusing on a mathematical proof and does not provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The provided text is an appendix focusing on a mathematical proof and does not specify dataset splits for training, validation, or testing. |
| Hardware Specification | No | The provided text is an appendix focusing on a mathematical proof and does not provide specific hardware details used for running experiments. |
| Software Dependencies | No | The provided text is an appendix focusing on a mathematical proof and does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The provided text is an appendix focusing on a mathematical proof and does not contain specific experimental setup details or hyperparameters. |