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A Particle-based Sparse Gaussian Process Optimizer
Task learning in neural networks typically requires finding a globally optimal minimizer to a loss function objective.
Hamiltonian Monte Carlo Particle Swarm Optimizer
We introduce the Hamiltonian Monte Carlo Particle Swarm Optimizer (HMC-PSO), an optimization algorithm that reaps the benefits of both Exponentially Averaged Momentum PSO and HMC sampling.
A Swarm Variant for the Schrödinger Solver
This paper introduces application of the Exponentially Averaged Momentum Particle Swarm Optimization (EM-PSO) as a derivative-free optimizer for Neural Networks.
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