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Found 7 papers, 5 papers with code
Unsupervised Music Source Separation Using Differentiable Parametric Source Models
Integrating domain knowledge in the form of source models into a data-driven method leads to high data efficiency: the proposed approach achieves good separation quality even when trained on less than three minutes of audio.
Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections
The Sliced-Wasserstein distance (SW) is being increasingly used in machine learning applications as an alternative to the Wasserstein distance and offers significant computational and statistical benefits.
Approximate Bayesian Computation with the Sliced-Wasserstein Distance
Approximate Bayesian Computation (ABC) is a popular method for approximate inference in generative models with intractable but easy-to-sample likelihood.
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance
Minimum expected distance estimation (MEDE) algorithms have been widely used for probabilistic models with intractable likelihood functions and they have become increasingly popular due to their use in implicit generative modeling (e. g. Wasserstein generative adversarial networks, Wasserstein autoencoders).
Generalized Sliced Wasserstein Distances
The SW distance, specifically, was shown to have similar properties to the Wasserstein distance, while being much simpler to compute, and is therefore used in various applications including generative modeling and general supervised/unsupervised learning.
Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo
We illustrate our framework on the popular Stochastic Gradient Langevin Dynamics (SGLD) algorithm and propose a novel SG-MCMC algorithm referred to as Stochastic Gradient Richardson-Romberg Langevin Dynamics (SGRRLD).
Stochastic Quasi-Newton Langevin Monte Carlo
These second order methods directly approximate the inverse Hessian by using a limited history of samples and their gradients.
