Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Explaining Neural Matrix Factorization with Gradient Rollback
Authors: Carolin Lawrence, Timo Sztyler, Mathias Niepert4987-4995
AAAI 2021 | Venue PDF | 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 EMAIL |
| 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. |