1 code implementation • 31 May 2021 • Antonio Khalil Moretti, Liyi Zhang, Christian A. Naesseth, Hadiah Venner, David Blei, Itsik Pe'er
Bayesian phylogenetic inference is often conducted via local or sequential search over topologies and branch lengths using algorithms such as random-walk Markov chain Monte Carlo (MCMC) or Combinatorial Sequential Monte Carlo (CSMC).
2 code implementations • 8 Oct 2019 • Iddo Drori, Lu Liu, Yi Nian, Sharath C. Koorathota, Jie S. Li, Antonio Khalil Moretti, Juliana Freire, Madeleine Udell
We use these embeddings in a neural architecture to learn the distance between best-performing pipelines.
1 code implementation • 20 Sep 2019 • Antonio Khalil Moretti, Zizhao Wang, Luhuan Wu, Iddo Drori, Itsik Pe'er
We apply SVO to three nonlinear latent dynamics tasks and provide statistics to rigorously quantify the predictions of filtered and smoothed objectives.
no code implementations • 6 Nov 2018 • Daniel Hernandez, Antonio Khalil Moretti, Ziqiang Wei, Shreya Saxena, John Cunningham, Liam Paninski
We present Variational Inference for Nonlinear Dynamics (VIND), a variational inference framework that is able to uncover nonlinear, smooth latent dynamics from sequential data.
no code implementations • 27 Sep 2018 • Daniel Hernandez Diaz, Antonio Khalil Moretti, Ziqiang Wei, Shreya Saxena, John Cunningham, Liam Paninski
In the case of sequential data, closed-form inference is possible when the transition and observation functions are linear.