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Found 3 papers, 1 papers with code
A Fisher-Rao gradient flow for entropy-regularised Markov decision processes in Polish spaces
We study the global convergence of a Fisher-Rao policy gradient flow for infinite-horizon entropy-regularised Markov decision processes with Polish state and action space.
Mean-Field Langevin Dynamics and Energy Landscape of Neural Networks
Our work is motivated by a desire to study the theoretical underpinning for the convergence of stochastic gradient type algorithms widely used for non-convex learning tasks such as training of neural networks.
Unbiased deep solvers for linear parametric PDEs
We develop several deep learning algorithms for approximating families of parametric PDE solutions.
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