Title:Challenges and advances in the training of Energy-Based Models: theoretical insights on non-equilibrium sampling and overfitting Abstract In the context of unsupervised learning and generative models in artificial intelligence, energy-based models (EBMs) provide a simple way to describe arbitrary complex datasets, by encoding their empirical distribution into a Boltzmann probability law with a given energy […]