Search Results for author:
Found 3 papers, 2 papers with code
Interpretable VAEs for nonlinear group factor analysis
Deep generative models have recently yielded encouraging results in producing subjectively realistic samples of complex data.
Neural Granger Causality
We show that our neural Granger causality methods outperform state-of-the-art nonlinear Granger causality methods on the DREAM3 challenge data.
Variational Boosting: Iteratively Refining Posterior Approximations
We propose a black-box variational inference method to approximate intractable distributions with an increasingly rich approximating class.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
