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..
Tensor Decomposition with Missing Indices
Authors: Yuto Yamaguchi, Kohei Hayashi
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments on both synthetic and real datasets show that the proposed model achieves higher accuracy in the tensor completion task than baselines. |
| Researcher Affiliation | Academia | Yuto Yamaguchi , Kohei Hayashi AIST, Japan EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Variational MAP-EM algorithm for the proposed model with CP decomposition (3rd-order), Algorithm 2 Update-q(in), Algorithm 3 Update-U |
| Open Source Code | Yes | Reproducibility. Our code to reproduce the experiments is available at https://goo.gl/W4K86I. |
| Open Datasets | Yes | We use the Twitter dataset that is publicly available.1 This dataset consists of about 13M geotagged tweets. ... 1http://noisy-text.github.io/2016/geo-shared-task.html |
| Dataset Splits | No | For each index in Dtrain, we generate S samples from Gaussian distribution N( |Xijk, 1)... We randomly divide the samples into training set (70%) and test set (30%). The paper explicitly mentions training and testing sets, but no separate validation set or validation split percentage. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU, CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, specific libraries, frameworks). |
| Experiment Setup | Yes | The size of the tensors is 10 10 10. ... R = 3. ... S = 1 for small dataset, whereas S = 10 for large dataset. ... We vary α from 0 to 0.9 by the step size 0.1. ... Parameters are set to λ = 1.0 and R = 10. |