Apr 15, 2021 · This paper considers the situation where the function approximation is made either using the kernel method or the two-layer neural network model ...
This paper considers the situation where the function approximation is made either using the kernel method or the two-layer neural network model, ...
Feb 15, 2022 · This paper considers general kernel method and two-layer neural network models and establishes dimension-independent results for these two ...
[28] approximated deep ReLU networks using Besov classes; and Long et al. [22] analyzed two-layer neural networks based on neural tangent kernels or Barron ...
Missing: L2 | Show results with:L2
An L2 analysis of reinforcement learning in high dimensions with kernel and neural network approximation, Jihao Long, Jiequn Han, Weinan E, CSIAM ...
Jihao Long, Jiequn Han and Weinan E, "An L2 Analysis of Reinforcement Learning in High Dimensions with Kernel and Neural Network Approximation" , 2021. 57 ...
E, An L2 analysis of reinforcement learning in high dimensions with kernel and neural network approximation, CSIAM Trans. Appl. Math., 3(2):191–220, 2022 ...
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An l2 analysis of reinforcement learning in high dimensions with kernel and ... A finite-time analysis of q-learning with neural network function.
This paper reviews recent results on error analysis for reinforcement learning algorithms in linear or nonlinear approximation settings, ...
[PDF] Linearized two-layers neural networks in high dimension - NSF PAR
par.nsf.gov › servlets › purl
We study two popular classes of models that can be regarded as linearizations of two-layers neural networks around a random initialization: the random features ...
Missing: Reinforcement | Show results with:Reinforcement