Explaining Neural Matrix Factorization with Gradient Rollback
Authors: Carolin Lawrence, Timo Sztyler, Mathias Niepert4987-4995
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also conduct experiments which show that gradient rollback provides faithful explanations for knowledge base completion and recommender datasets. An implementation1 and an appendix2 are available. |
| Researcher Affiliation | Industry | Carolin Lawrence, Timo Sztyler, Mathias Niepert NEC Laboratories Europe, Kurf ursten-Anlage 36, 69115 Heidelberg, Germany {carolin.lawrence, timo.sztyler, mathias.niepert}@neclab.eu |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | An implementation1 and an appendix2 are available. 1https://github.com/carolinlawrence/gradient-rollback |
| Open Datasets | Yes | We report results on three datasets: two knowledge base completion (NATIONS (Kok and Domingos 2007), FB15K-237 (Toutanova et al. 2015)) and one recommendation dataset (MOVIELENS (Harper and Konstan 2015)). |
| Dataset Splits | No | The paper does not provide specific percentages or counts for training/validation splits. It mentions: "Since retraining is costly, we only explain the top-1 prediction for a set of 100 random test triples for both FB15K-237 and MOVIELENS. For NATIONS we use the entire test set." and "We fix all random seeds and use the same set of negative samples during (re-)training to avoid additional randomization effects." |
| Hardware Specification | Yes | For example, FB15K-237 contains 270k training triples and training one model with TF2 on a RTX 2080 Ti GPU takes about 15 minutes. |
| Software Dependencies | No | The paper mentions "TF2" implying TensorFlow 2.x, but does not provide a specific version number. It does not list other software dependencies with version numbers. |
| Experiment Setup | No | The paper states: "Statistics and hyperparameter settings are in the appendix." and "We fix all random seeds and use the same set of negative samples during (re-)training to avoid additional randomization effects." While the latter is a setup detail in the main text, specific hyperparameter values are deferred to the appendix and not present in the main text. |