4 papers
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…
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…
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…
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…