A stochastic gradient flow on particle swarms driven by a softmin energy approximation converges to global minima for strongly convex functions and exhibits faster hitting times between wells than overdamped Langevin dynamics.
Active bias: Training more accurate neural networks by emphasizing high variance samples.Advances in Neural Information Processing Systems, 30, 2017
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Global Optimization via Softmin Energy Minimization
A stochastic gradient flow on particle swarms driven by a softmin energy approximation converges to global minima for strongly convex functions and exhibits faster hitting times between wells than overdamped Langevin dynamics.