1 citations · 1 across the 5 of their papers we have counts for
5 papers
Efficient Online Variational Estimation via Monte Carlo Sampling
Mathis Chagneux, Mathias Müller, Pierre Gloaguen +2
This article addresses online variational estimation in parametric state-space models. We propose a new procedure for efficiently computing the evidence lower bound and its gradien…
Controllable protein design with particle-based Feynman-Kac steering
Erik Hartman, Jonas Wallin, Johan Malmström +1
Proteins underpin most biological function, and the ability to design them with tailored structures and properties is central to advances in biotechnology. Diffusion-based generati…
Recursive Learning of Asymptotic Variational Objectives
Alessandro Mastrototaro, Mathias Müller, Jimmy Olsson
General state-space models (SSMs) are widely used in statistical machine learning and are among the most classical generative models for sequential time-series data. SSMs, comprisi…
Importance sampling for online variational learning
Mathis Chagneux, Pierre Gloaguen, Sylvain Le Corff +1
This article addresses online variational estimation in state-space models. We focus on learning the smoothing distribution, i.e. the joint distribution of the latent states given…
Online Variational Sequential Monte Carlo
Alessandro Mastrototaro, Jimmy Olsson
Being the most classical generative model for serial data, state-space models (SSM) are fundamental in AI and statistical machine learning. In SSM, any form of parameter learning o…