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..
Data Continuity Matters: Improving Sequence Modeling with Lipschitz Regularizer
Authors: Eric Qu, Xufang Luo, Dongsheng Li
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various tasks demonstrate that altering data continuity via Lipschitz Regularizer can largely improve the performance of many deep models for sequence modeling.1 |
| Researcher Affiliation | Collaboration | Eric Qu Duke Kunshan University Kunshan, Jiangsu 215316, China EMAIL Xufang Luo & Dongsheng Li Microsoft Research Asia Shanghai 200232, China EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1Code is available at https://Eric Qu.site/Lip Reg/ |
| Open Datasets | Yes | We use 5 datasets in this experiment and their descriptions are in Appendix C.1. Evaluation metrics are Mean Square Error (MSE) and Mean Absolute Error (MAE). |
| Dataset Splits | Yes | Hyperparameter λ is chosen from {1, 2, 3, 4, 5} when the model performs best on the validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | Hyperparameter λ is chosen from {1, 2, 3, 4, 5} when the model performs best on the validation set. Hyperparameter λ is chosen from {1, 2, 3, 4, 5, 6, 7, 8} when the model performs best on the validation set. |