17 citations · 29 across the 6 of their papers we have counts for
6 papers
Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic Space
Alex Chen, Philipe Chlenski, Kenneth Munyuza +3
Hyperbolic space naturally encodes hierarchical structures such as phylogenies (binary trees), where inward-bending geodesics reflect paths through least common ancestors, and the…
Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics
Hanming Yang, Antonio Khalil Moretti, Sebastian Macaluso +3
Reconstructing jets, which provide vital insights into the properties and histories of subatomic particles produced in high-energy collisions, is a main problem in data analyses in…
Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference
Antonio Khalil Moretti, Liyi Zhang, Christian A. Naesseth +3
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 (…
Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations
Iddo Drori, Darshan Thaker, Arjun Srivatsa +15
Proteins are the major building blocks of life, and actuators of almost all chemical and biophysical events in living organisms. Their native structures in turn enable their biolog…
AutoML using Metadata Language Embeddings
Iddo Drori, Lu Liu, Yi Nian +5
As a human choosing a supervised learning algorithm, it is natural to begin by reading a text description of the dataset and documentation for the algorithms you might use. We demo…
Particle Smoothing Variational Objectives
Antonio Khalil Moretti, Zizhao Wang, Luhuan Wu +2
A body of recent work has focused on constructing a variational family of filtered distributions using Sequential Monte Carlo (SMC). Inspired by this work, we introduce Particle Sm…