Energy-Based Models (EBMs) are a learning framework that assigns a quality score to any given input, its energy; contrary to
probabilistic models, there is no a priori requirement that these scores be normalized (i.e. sum to one). Energies are typically
computed through a neural network, and training an EBM corresponds to shaping the energy function such that data points nearby the underlying data manifold are associated with lower energies than data points that are far from it. Not imposing normalization affords a great power and flexibility to the modelling process, e.g. in terms of combining energies, on conditioning on certain variables, of computing global scores on complex structured objects, or on expressing prior
knowledge. However, this freedom comes with significant technical challenges, in terms of learning and inference.

A strong comeback of EBMs is currently underway. This ICLR-2021 Workshop is the opportunity to increase awareness about the diversity of works in this area, to discuss current challenges, and to encourage cross-pollination between different communities around this topic.