On the Random Conjugate Kernel and Neural Tangent Kernel
Authors: Zhengmian Hu, Heng Huang
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The numerical experiments are conducted and all results validate the soundness of our theoretical analysis. |
| Researcher Affiliation | Academia | Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15213, USA. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing code or links to source code repositories. |
| Open Datasets | No | The paper mentions using the 'UCI dataset' in Section C.5, but does not provide concrete access information such as a direct link, DOI, repository name, or formal citation for the dataset itself. |
| Dataset Splits | No | The paper describes experimental setups involving sampling networks and reinitializing them (e.g., 'We sample 1000 three layers feedforward network', 'We reinitialize these networks 10^6 times'), but it does not specify explicit train/validation/test dataset splits from a pre-existing dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. It only mentions general experimental procedures. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers needed to replicate the experiments. |
| Experiment Setup | Yes | The hyper-parameters are set as n0 = n1 = n2 = 100, n2 = 1 and σi = 1 10. The input x0 is sampled from a unit sphere. |