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 [1].
Normalized Spectral Map Synchronization
Authors: Yanyao Shen, Qixing Huang, Nati Srebro, Sujay Sanghavi
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the usefulness of Norm Spec Sync on both synthetic and real datasets. |
| Researcher Affiliation | Academia | Yanyao Shen UT Austin Austin, TX 78712 EMAIL Qixing Huang TTI Chicago and UT Austin Austin, TX 78712 EMAIL Nathan Srebro TTI Chicago Chicago, IL 60637 EMAIL Sujay Sanghavi UT Austin Austin, TX 78712 EMAIL |
| Pseudocode | Yes | Algorithm 1 Norm Spec Sync Input: Xobs based on (1), δmax Initialize W0: set W0 as an initial guess for the top-m orthonormal eigenvectors, k 0 while W (k) W (k 1) > δmax do W (k+1)+ = Xobs W (k), W (k+1)R(k+1) = W (k+1)+, (QR factorization), k k + 1. end while Set W = W (k) and X spec i1 = (WW T )i1. Round each X spec i1 into the corresponding Xi1 by solving (3). Output: Xij = XT j1Xi1, 1 i, j n. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We first evaluate Norm Spec Sync on CMU Hotel and CMU House datasets [20]. |
| Dataset Splits | No | The paper mentions generating synthetic data and using CMU Hotel/House and SCAPE datasets, but does not provide specific details on how these datasets were split into training, validation, or test sets (e.g., percentages, sample counts, or cross-validation folds). |
| Hardware Specification | No | The paper reports computation times (e.g., 'The averaged running time for Spec Sync is 2.25 second') but does not specify the hardware components (e.g., CPU, GPU models, or memory) used for these experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies, libraries, or programming languages used in the experiments. |
| Experiment Setup | No | The paper describes the algorithm and general experimental procedures, but it does not provide specific details on the experimental setup, such as hyperparameter values (e.g., learning rate, batch size) or other training configuration settings. |