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..
Expected Tensor Decomposition with Stochastic Gradient Descent
Authors: Takanori Maehara, Kohei Hayashi, Ken-ichi Kawarabayashi
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results confirm that our algorithms significantly outperform all existing methods in terms of accuracy. We also show that they can successfully decompose a large tensor, containing billion-scale nonzero elements. 6 Experiments Throughout the experiments, the regularization parameter was fixed as ρ = 0.0001. All experiments were conducted using an Intel Xeon E5-2690 2.90GHz CPU with 256GB memory and Ubuntu 12.04. |
| Researcher Affiliation | Academia | Takanori Maehara1,3 Kohei Hayashi2,3 Ken-ichi Kawarabayashi2,3 1) Shizuoka University, Shizuoka, Japan 2) National Institute of Informatics, Tokyo, Japan 3) JST, ERATO, Kawarabayashi Large Graph Project |
| Pseudocode | No | The paper describes algorithms using mathematical equations but does not include pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide concrete access to source code or explicitly state its availability. |
| Open Datasets | Yes | We employed the Amazon review dataset4 (Mc Auley and Leskovec 2013), which contains 34 million user reviews. http://snap.stanford.edu/data/web-Amazon.html |
| Dataset Splits | No | The paper describes the datasets used (Amazon review dataset) and their sizes, but does not provide specific details on training, validation, or testing splits. |
| Hardware Specification | Yes | All experiments were conducted using an Intel Xeon E5-2690 2.90GHz CPU with 256GB memory and Ubuntu 12.04. |
| Software Dependencies | Yes | Our algorithm was implemented in C++ and compiled using g++v4.6 with -O3 option. |
| Experiment Setup | Yes | Throughout the experiments, the regularization parameter was fixed as ρ = 0.0001. For Figure 1 (a), we used the fixed step size rule η(t) = 1/(1 + t) and for Figure 1 (b), we used η(t) = λ0/(t0 + t), where parameters λ0 and t0 were optimized by a grid search. For efficient computation, we used the mini-batch method, i.e., each sample was a sum of the tensors of 1000 reviews. |