5 citations · 10 across the 2 of their papers we have counts for
3 papers
Interpreting Neural Networks through the Polytope Lens
Sid Black, Lee Sharkey, Leo Grinsztajn +8
Mechanistic interpretability aims to explain what a neural network has learned at a nuts-and-bolts level. What are the fundamental primitives of neural network representations? Pre…
Bayesian workflow for disease transmission modeling in Stan
Léo Grinsztajn, Elizaveta Semenova, Charles C. Margossian +1
This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the SARS-CoV-2 pandemic and oth…
MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference
Achille Thin, Nikita Kotelevskii, Jean-Stanislas Denain +4
In this contribution, we propose a new computationally efficient method to combine Variational Inference (VI) with Markov Chain Monte Carlo (MCMC). This approach can be used with g…