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 [1].
The Implicit Regularization of Momentum Gradient Descent in Overparametrized Models
Authors: Li Wang, Zhiguo Fu, Yingcong Zhou, Zili Yan
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The numerical experiments strongly support our theoretical results. |
| Researcher Affiliation | Academia | 1School of Computer Science and Information Technology & KLAS, Northeast Normal University, China 2School of Mathematics and Statistics, Beihua University, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper mentions generating features for experiments (e.g., "We generate features via X = Σ1/2Z...") rather than using a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper does not provide specific dataset split information for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | Fix the same sufficiently small initialization w0 = 0.01 and step size ϵ = 0.001, we plot the path of w for different N = 1, 2, 3, 4. |