Statements (33)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:mathematical_concept
learning theory |
| gptkbp:abbreviation |
gptkb:NTK
|
| gptkbp:appliesTo |
convolutional neural networks
recurrent neural networks fully connected neural networks |
| gptkbp:arXivID |
1806.07572
|
| gptkbp:basisFor |
NTK regression
NTK-based generalization bounds NTK-based optimization analysis |
| gptkbp:describes |
infinite-width neural networks
|
| gptkbp:enables |
closed-form solutions for training dynamics
linearization of neural network training |
| gptkbp:field |
gptkb:machine_learning
gptkb:mathematics deep learning |
| https://www.w3.org/2000/01/rdf-schema#label |
Neural Tangent Kernel
|
| gptkbp:influenced |
theoretical deep learning research
|
| gptkbp:introduced |
Arthur Jacot
Clément Hongler Franck Gabriel |
| gptkbp:introducedIn |
2018
|
| gptkbp:limitingCaseOf |
infinite-width neural networks
|
| gptkbp:publishedIn |
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
|
| gptkbp:relatedTo |
gptkb:Gaussian_processes
gptkb:kernel_methods gradient descent function space view random initialization |
| gptkbp:usedFor |
theoretical analysis of neural networks
analyzing training dynamics |
| gptkbp:bfsParent |
gptkb:NTK
|
| gptkbp:bfsLayer |
5
|