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..
On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective
Authors: Lili Su, Pengkun Yang
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A numerical illustration of the decay of Ξ»min(H) in n is presented in Fig. 1a. A numerical illustration of the spectrum concentration of K is given in Fig. 1b; Training with f being randomly generated linear or quadratic functions with n = 1000, m = 2000. |
| Researcher Affiliation | Academia | Lili Su CSAIL, MIT EMAIL Pengkun Yang Department of Electrical Engineering Princeton University EMAIL |
| Pseudocode | No | The paper describes the gradient descent update rules and initialization steps in paragraph text and mathematical equations (e.g., (3), (5)) but does not include a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code related to the described methodology. |
| Open Datasets | No | The paper describes data generation from a distribution (e.g., 'uniform distribution on the spheres') and mentions 'training with f being randomly generated linear or quadratic functions', but it does not specify or provide access information (link, citation to a public dataset) for any publicly available dataset used for its numerical illustrations. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits. It mentions 'training dataset' but no specific percentages or counts for different splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for its numerical illustrations or computations. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | For each k = 1, , m/2: Initialize w2k 1 N(0, I), and a2k 1 = 1 with probability 1/2, and a2k 1 = -1 with probability 1/2. Initialize w2k = w2k 1 and a2k = a2k 1. All randomnesses in this initialization are independent, and are independent of the dataset. where > 0 is stepsize/learning rate. Training with f being randomly generated linear or quadratic functions with n = 1000, m = 2000. |