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].
Neural tangent kernels, transportation mappings, and universal approximation
Authors: Ziwei Ji, Matus Telgarsky, Ruicheng Xian
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper establishes rates of universal approximation for the shallow neural tangent kernel (NTK): network weights are only allowed microscopic changes from random initialization, which entails that activations are mostly unchanged, and the network is nearly equivalent to its linearization. Concretely, the paper has two main contributions: a generic scheme to approximate functions with the NTK by sampling from transport mappings between the initial weights and their desired values, and the construction of transport mappings via Fourier transforms. |
| Researcher Affiliation | Academia | Ziwei Ji, Matus Telgarsky, Ruicheng Xian Department of Computer Science University of Illinois at Urbana-Champaign EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any statement or link about the availability of open-source code for the methodology described. |
| Open Datasets | No | No datasets are used as the paper is purely theoretical. Therefore, no information about public dataset access is provided. |
| Dataset Splits | No | No dataset split information is provided as the paper is purely theoretical and does not involve empirical evaluation. |
| Hardware Specification | No | No hardware specifications are mentioned as the paper is purely theoretical and does not describe any experiments. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned, as the paper is theoretical and does not describe experimental implementations. |
| Experiment Setup | No | No experimental setup details or hyperparameters are provided, as the paper is theoretical and does not involve empirical evaluation. |