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..
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
Authors: Arthur Jacot, Franck Gabriel, Clement Hongler
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally we study the NTK numerically, observe its behavior for wide networks, and compare it to the infinite-width limit. In the following numerical experiments, fully connected ANNs of various widths are compared to the theoretical infinite-width limit. |
| Researcher Affiliation | Academia | Arthur Jacot Ecole Polytechnique F ed erale de Lausanne EMAIL Franck Gabriel Imperial College London and Ecole Polytechnique F ed erale de Lausanne EMAIL Cl ement Hongler Ecole Polytechnique F ed erale de Lausanne EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks are provided in the paper. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We now illustrate our result on the MNIST dataset of handwritten digits made up of grayscale images of dimension 28 28, yielding a dimension of n0 = 784. |
| Dataset Splits | No | The paper mentions using the MNIST dataset and an artificial dataset, but does not specify any training, validation, or test dataset splits or cross-validation methodology. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for experiments are mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers for any libraries or frameworks used in the experiments. |
| Experiment Setup | Yes | In our numerical experiments, we take β = 0.1 and use a learning rate of 1.0, which is larger than usual, see Section 6. This gives a behaviour similar to that of a classical network of width 100 with a learning rate of 0.01. After 200 steps of gradient descent with learning rate 1.0 (i.e. at t = 200). for 1000 steps with learning rate 1.0 |