Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Input Tokens: 16,657 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,252 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |