Tensor Decomposition with Missing Indices
Authors: Yuto Yamaguchi, Kohei Hayashi
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 yuto.ymgc@gmail.com, hayashi.kohei@gmail.com |
| 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. |