AI RESEARCH
A Diffusive Classification Loss for Learning Energy-based Generative Models
arXiv CS.LG
•
ArXi:2601.21025v3 Announce Type: replace-cross Score-based generative models have recently achieved remarkable success. While they are usually parameterized by the score, an alternative way is to use a series of time-dependent energy-based models (EBMs), where the score is obtained from the negative input-gradient of the energy. Crucially, EBMs can be leveraged not only for generation, but also for tasks such as compositional sampling or building Boltzmann Generators via Monte Carlo methods. However