Expected Tensor Decomposition with Stochastic Gradient Descent
Authors: Takanori Maehara, Kohei Hayashi, Ken-ichi Kawarabayashi
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | 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. |