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Found 5 papers, 1 papers with code
Global Convergence of Second-order Dynamics in Two-layer Neural Networks
Recent results have shown that for two-layer fully connected neural networks, gradient flow converges to a global optimum in the infinite width limit, by making a connection between the mean field dynamics and the Wasserstein gradient flow.
Reflected Schrödinger Bridge: Density Control with Path Constraints
How to steer a given joint state probability density function to another over finite horizon subject to a controlled stochastic dynamics with hard state (sample path) constraints?
Hopfield Neural Network Flow: A Geometric Viewpoint
We provide gradient flow interpretations for the continuous-time continuous-state Hopfield neural network (HNN).
Gradient Flow Algorithms for Density Propagation in Stochastic Systems
The need for computing the transient joint PDFs subject to prior dynamics arises in uncertainty propagation, nonlinear filtering and stochastic control.
Proximal Recursion for Solving the Fokker-Planck Equation
We develop a new method to solve the Fokker-Planck or Kolmogorov's forward equation that governs the time evolution of the joint probability density function of a continuous-time stochastic nonlinear system.
Optimization and Control
