Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Input Tokens: 7,444 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,900 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |