Canonical Tensor Decomposition for Knowledge Base Completion
Authors: Timothee Lacroix, Nicolas Usunier, Guillaume Obozinski
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform all our experiments on 5 common benchmark datasets of link prediction in knowledge bases. |
| Researcher Affiliation | Collaboration | 1Facebook AI Research, Paris, France 2Université Paris-Est, Equipe Imagine, LIGM (UMR8049) Ecole des Ponts Paris Tech Marne-la-Vallée, France. |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block found in the paper. |
| Open Source Code | Yes | The code is available at https://github.com/ facebookresearch/kbc. |
| Open Datasets | Yes | WN18 and FB15K are popular benchmarks in the Knowledge Base Completion community. |
| Dataset Splits | Yes | We used the train/valid/test splits provided with these datasets and measured the filtered Mean Reciprocal Rank (MRR) and Hits@10 (Bordes et al. (2013)). |
| Hardware Specification | Yes | We conducted all experiments on a Quadro GP 100 GPU. |
| Software Dependencies | No | The paper mentions optimizers like Adagrad and Adam but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | Our grid consisted of two learning rates: 10 1 and 10 2, two batch-sizes: 25 and 100, and regularization coefficients in [0, 10 3, 5.10 3, 10 2, 5.10 2, 10 1, 5.10 1]. ... We trained for 100 epochs to ensure convergence |