10 citations · 23 across the 5 of their papers we have counts for
10 papers
Diffusion Priors In Variational Autoencoders
Antoine Wehenkel, Gilles Louppe
Among likelihood-based approaches for deep generative modelling, variational autoencoders (VAEs) offer scalable amortized posterior inference and fast sampling. However, VAEs are a…
A deep generative model for probabilistic energy forecasting in power systems: normalizing flows
Jonathan Dumas, Antoine Wehenkel Damien Lanaspeze, Bertrand Cornélusse +1
Greater direct electrification of end-use sectors with a higher share of renewables is one of the pillars to power a carbon-neutral society by 2050. However, in contrast to convent…
A Probabilistic Forecast-Driven Strategy for a Risk-Aware Participation in the Capacity Firming Market: extended version
Jonathan Dumas, Colin Cointe, Antoine Wehenkel +3
This paper addresses the energy management of a grid-connected renewable generation plant coupled with a battery energy storage device in the capacity firming market, designed to p…
Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference
Maxime Vandegar, Michael Kagan, Antoine Wehenkel +1
We revisit empirical Bayes in the absence of a tractable likelihood function, as is typical in scientific domains relying on computer simulations. We investigate how the empirical…
Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization
Arnaud Delaunoy, Antoine Wehenkel, Tanja Hinderer +4
Gravitational waves from compact binaries measured by the LIGO and Virgo detectors are routinely analyzed using Markov Chain Monte Carlo sampling algorithms. Because the evaluation…
You say Normalizing Flows I see Bayesian Networks
Antoine Wehenkel, Gilles Louppe
Normalizing flows have emerged as an important family of deep neural networks for modelling complex probability distributions. In this note, we revisit their coupling and autoregre…