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..
Near-Isometric Properties of Kronecker-Structured Random Tensor Embeddings
Authors: Qijia Jiang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we numerically investigate (1) embedding dimension scaling with d for the two types of random embeddings in Section 4 and 5; (2) signal recovery from random Gaussian measurements as elaborated in Section 6.1 where the signal is rank-1 belonging to a product of cones. |
| Researcher Affiliation | Academia | Qijia Jiang Lawrence Berkeley National Laboratory EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. |
| Open Source Code | No | The paper references a 'Matlab Tensor Toolbox' with a URL, but this is a third-party tool, not the authors' own source code for their methodology. No explicit statement or link to their own code was provided. |
| Open Datasets | No | For the first experiment, we let n = 10, d = 5 and pick each factor {uj} to be 20% sparse. The figure below reports the average distortion of the embedding |k Sxk2 1| over 25 runs for both the row-wise tensored and recursive sketch with Gaussian random factors. |
| Dataset Splits | No | No specific dataset split information (percentages, sample counts, or citations to predefined splits) for training, validation, or testing was found. |
| Hardware Specification | No | The paper's own checklist explicitly states 'N/A' for hardware specification, and no specific hardware details were found in the text. |
| Software Dependencies | No | We use the tucker-als function from the Matlab Tensor Toolbox1 for computing the best rank-(1, 1, 1) tensor approximation, after which gradient update is made on each factor followed by 1 projection. (1http://www.tensortoolbox.org) |
| Experiment Setup | Yes | For the first experiment, we let n = 10, d = 5 and pick each factor {uj} to be 20% sparse. The figure below reports the average distortion of the embedding |k Sxk2 1| over 25 runs for both the row-wise tensored and recursive sketch with Gaussian random factors. ... We set each factor {uj} to be 20% sparse and let d = 3, n = 10, m = 2 0.8 n d for 2 {1, , 3} and record the successful recovery out of 25 trials. Stepsize is picked to be 0.1 and success is defined as L(z1, , zd) 0.1 after 500 gradient steps. |