Applications of Fokker Planck equations in machine learning algorithms

DOI10.1007/978-3-031-29875-2_10zbMATH Open1548.6525MaRDI QIDQ6613483

Yuhua Zhu

Publication date: 2 October 2024

zbMATH Keywords

fairnessFokker-Planck equationsreinforcement learningmachine learning algorithmsstochastic gradient descent

Mathematics Subject Classification ID

Computational learning theory (68Q32) Artificial neural networks and deep learning (68T07) Numerical mathematical programming methods (65K05) Stochastic programming (90C15) Stochastic approximation (62L20) PDEs with randomness, stochastic partial differential equations (35R60) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65M99) Fokker-Planck equations (35Q84) PDEs on time scales (35R07)

Cites Work

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• On the diffusion approximation of nonconvex stochastic gradient descent
• On the performance of the Euler-Maruyama scheme for SDEs with discontinuous drift coefficient
• Perturbed Iterate Analysis for Asynchronous Stochastic Optimization
• Stochastic modified equations for the asynchronous stochastic gradient descent
• Stochastic Processes and Applications

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