LowFER: Low-rank Bilinear Pooling for Link Prediction
Authors: Saadullah Amin, Stalin Varanasi, Katherine Ann Dunfield, Günter Neumann
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we evaluate on real-world datasets, reaching on par or state-of-the-art performance. At extreme low-ranks, model preserves the performance while staying parameter efficient. |
| Researcher Affiliation | Academia | 1German Research Center for Artificial Intelligence (DFKI), Saarbr ucken, Germany 2Department of Language Science and Technology, Saarland University, Saarbr ucken, Germany. |
| Pseudocode | No | The paper provides mathematical equations and figures illustrating the model, but it does not contain a structured pseudocode block or algorithm section. |
| Open Source Code | No | The authors state: "The authors would like to thank the anonymous reviewers for helpful feedback and gratefully acknowledge the use of code released by Balaˇzevi c et al. (2019a)." This refers to a third-party's code, not their own source code for the described methodology. |
| Open Datasets | Yes | We conducted the experiments on four benchmark datasets: WN18 (Bordes et al., 2013), WN18RR (Dettmers et al., 2018), FB15k (Bordes et al., 2013) and FB15k-237 (Toutanova et al., 2015) (see Appendix B for the details, including best hyperparameters and additional experiments). |
| Dataset Splits | No | The paper mentions creating a "training set D" and discusses "test set results," but it does not specify explicit train/validation/test dataset splits (e.g., percentages or sample counts for each split) within the main text. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., GPU models, CPU types, or memory specifications). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software components or libraries used in the experiments. |
| Experiment Setup | Yes | To train the Low FER model, we follow the setup of Balaˇzevi c et al. (2019a). ... For de = 200 and dr = 30, we vary k from {1, 5, 10, 30, 50, 100, 150, 200} on FB15k... ... we trained our models on FB15k, with dr = 30, k = 50 constant, and varying de in {30, 50, 100, 150, 200, 250, 300, 350, 400}. ... with dr = 50 at k = 150 and l2-regularization 0.0005. |