tangent kernel
Deep Learning Through AT elescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond Alan Jeffares
Deep learning sometimes appears to work in unexpected ways.
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On Exact Computation with an Infinitely Wide Neural Net
Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Russ R. Salakhutdinov, Ruosong Wang
Moreo randominitializationH( 0)conv deterministic H asthewidthNeur ker ( , ) (Equation (2)) evaluatedH(t)= H forallt, then (3) becomes du(t) dt = H (u(t) y). Suppose (z)= max ( 0,z), 1/ = poly ( 1/ ,log (n / )) and d1 = d2 = = dL = m with m poly ( 1/ , L,1/ 0,n,log ( 1/ )).
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- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
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- Information Technology > Data Science > Data Mining (0.93)
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Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.94)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.72)
ExtrapolationandSpectralBiasofNeuralNetswith HadamardProduct:aPolynomialNetStudy
Weprovetheir equivalence to the kernel regression predictor with the associated NTK, which expands the application scope of NTK. Based on our results, we elucidate the separation ofPNNs overstandard neural networks with respect toextrapolation andspectralbias.
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)
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Onthe Equivalencebetween Neural Networkand Support Vector Machine
Suppose l is -lipschitzand l-smoothforthefirstargument (i.e.
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Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Support Vector Machines (0.86)