1 citations · 2 across the 6 of their papers we have counts for
7 papers
Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models
Maksym Tretiakov, Sarah Filippi, Sarah Lucie Filipp +4
Many machine learning methods aim to approximate the lower-dimensional manifold on which the data lives. A desirable feature of such methods is that they should capture the epistem…
Emyx: Fast and efficient all-atom protein generation
Nicholas J. Williams, Ward Haddadin, Matteo P. Ferla +7
Computational enzyme design requires generating proteins that scaffold catalytic residues and ligands, a task that demands both geometric accuracy and structural diversity from the…
Continuous Bayesian Model Selection for Multivariate Causal Discovery
Anish Dhir, Ruby Sedgwick, Avinash Kori +2
Current causal discovery approaches require restrictive model assumptions in the absence of interventional data to ensure structure identifiability. These assumptions often do not…
Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic Assays
Ruby Sedgwick, John P. Goertz, Molly M. Stevens +2
With the rise in engineered biomolecular devices, there is an increased need for tailor-made biological sequences. Often, many similar biological sequences need to be made for a sp…
Weighted-Sum of Gaussian Process Latent Variable Models
James Odgers, Ruby Sedgwick, Chrysoula Kappatou +2
This work develops a Bayesian non-parametric approach to signal separation where the signals may vary according to latent variables. Our key contribution is to augment Gaussian Pro…
Gaussian Processes for Monitoring Air-Quality in Kampala
Clara Stoddart, Lauren Shrack, Richard Sserunjogi +6
Monitoring air pollution is of vital importance to the overall health of the population. Unfortunately, devices that can measure air quality can be expensive, and many cities in lo…
Design of Experiments for Verifying Biomolecular Networks
Ruby Sedgwick, John Goertz, Molly Stevens +2
There is a growing trend in molecular and synthetic biology of using mechanistic (non machine learning) models to design biomolecular networks. Once designed, these networks need t…