5 papers
Leveraging generative models to assist Monte Carlo sampling
Marylou Gabrié
Sampling high-dimensional probability distributions is a central task in scientific computing, with applications ranging from Bayesian inference to statistical physics and molecula…
Sampling metastable systems using collective variables and Jarzynski-Crooks paths
Christoph Schönle, Marylou Gabrié, Tony Lelièvre +1
We consider the problem of sampling a high dimensional multimodal target probability measure. We assume that a good proposal kernel to move only a subset of the degrees of freedoms…
A theoretical perspective on mode collapse in variational inference
Roman Soletskyi, Marylou Gabrié, Bruno Loureiro
While deep learning has expanded the possibilities for highly expressive variational families, the practical benefits of these tools for variational inference (VI) are often limite…
Coarse Grained Molecular Dynamics with Normalizing Flows
Samuel Tamagnone, Alessandro Laio, Marylou Gabrié
We propose a sampling algorithm relying on a collective variable (CV) of mid-size dimension modelled by a normalizing flow and using non-equilibrium dynamics to propose full config…
Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracy
Ana Molina-Taborda, Pilar Cossio, Olga Lopez-Acevedo +1
Extracting consistent statistics between relevant free-energy minima of a molecular system is essential for physics, chemistry and biology. Molecular dynamics (MD) simulations can…