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.
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel
Authors: Colin Wei, Jason D. Lee, Qiang Liu, Tengyu Ma
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 19,508 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,134 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 | Finally, we empirically validate several claims made in this paper in Section 5. First, we con๏ฌrm on synthetic data that neural networks do generalize better with an explicit regularizer vs. without. Second, we show that for two-layer networks, the test error decreases and margin increases as the hidden layer grows, as predicted by our theory. |
| Researcher Affiliation | Academia | Colin Wei Department of Computer Science Stanford University EMAIL Jason D. Lee Department of Electrical Engineering Princeton University EMAIL Qiang Liu Department of Computer Science University of Texas at Austin EMAIL Tengyu Ma Department of Computer Science Stanford University EMAIL |
| 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 in this paper. |
| Open Datasets | No | The data comes from a ground truth network with 10 hidden networks, input dimension 20, and a ground truth unnormalized margin of at least 0.01. |
| Dataset Splits | No | The paper mentions a 'training set of size 200' but does not provide specific details on training/validation/test splits, percentages, or sample counts. |
| 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 (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We use a training set of size 200 and train for 20000 steps with learning rate 0.1, once using regularizer ฮป = 5 10 4 and once using regularization ฮป = 0. |