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Annealed Stein Variational Gradient Descent
[article]
2021
arXiv
pre-print
Particle based optimization algorithms have recently been developed as sampling methods that iteratively update a set of particles to approximate a target distribution. In particular Stein variational gradient descent has gained attention in the approximate inference literature for its flexibility and accuracy. We empirically explore the ability of this method to sample from multi-modal distributions and focus on two important issues: (i) the inability of the particles to escape from local
arXiv:2101.09815v3
fatcat:tckewlv22bfh7bkleykwoo7q7i