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..
Approximate Multiplication of Sparse Matrices with Limited Space
Authors: Yuanyu Wan, Lijun Zhang10058-10066
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we perform numerical experiments to verify the efficiency and effectiveness of our SCOD. Fig. 1 and 2 show the results of different algorithms among different ℓon the synthetic datasets. |
| Researcher Affiliation | Academia | Yuanyu Wan, Lijun Zhang National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China EMAIL |
| Pseudocode | Yes | Algorithm 1 Dense Shrinkage (DS), Algorithm 2 Simultaneous Iteration (SI), Algorithm 3 Verified Simultaneous Iteration (VSI), Algorithm 4 Sparse Co-occuring Directions (SCOD) |
| Open Source Code | No | The paper does not provide a specific repository link or an explicit statement about the release of source code for the described methodology. |
| Open Datasets | Yes | NIPS conference papers1 (Perrone et al. 2017) and Movie Lens 10M2. ... 1https://archive.ics.uci.edu/ml/datasets/NIPS+Conference+ Papers+1987-2015 2https://grouplens.org/datasets/movielens/10m/ |
| Dataset Splits | No | The paper describes how the input matrices X and Y are derived from the original datasets (e.g., 'let XT be the first 2905 columns of M, and let Y T be the others'), but it does not specify train/validation/test splits for model evaluation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Matlab' as used for generating synthetic datasets but does not specify a version number or list other software dependencies with their versions for the implementation of the algorithm. |
| Experiment Setup | Yes | In all experiments, each algorithm will receive two matrices X Rmx n and Y Rmy n, and then output two matrices BX Rmx ℓand BY Rmy ℓ. We adopt the approximation error XY T BXBT Y and the projection error XY T π U(X)π V (Y )T to measure the accuracy of each algorithm, where U Rmx k, V Rmy k and we set k = 200. Furthermore, we report the runtime of each algorithm to verify the efficiency of our SCOD. Because of the randomness of SCOD, SFD-AMM, CS, RP and Hashing, we report the average results over 50 runs. |