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stat.ML2022★ 6 cited
Learning and Inference in Sparse Coding Models with Langevin Dynamics
Michael Y. -S. Fang, Mayur Mudigonda, Ryan Zarcone +2
We describe a stochastic, dynamical system capable of inference and learning in a probabilistic latent variable model. The most challenging problem in such models - sampling the po…
stat.ML2015★ 1 cited
A Markov Jump Process for More Efficient Hamiltonian Monte Carlo
Andrew B. Berger, Mayur Mudigonda, Michael R. DeWeese +1
In most sampling algorithms, including Hamiltonian Monte Carlo, transition rates between states correspond to the probability of making a transition in a single time step, and are…