Energy-Based Models with Applications to Speech and Language Processing

arXiv:2403.10961v1 Announce Type: new
Abstract: Energy-Based Models (EBMs) are an important class of probabilistic models, also known as random fields and undirected graphical models. EBMs are un-normalized and thus radically different from other popular self-normalized probabilistic models such as hidden Markov models (HMMs), autoregressive models, generative adversarial nets (GANs) and variational auto-encoders (VAEs). Over the past years, EBMs have attracted increasing interest not only from the core machine learning community, but also from application domains such as speech, vision, natural …

abstract adversarial applications arxiv autoregressive models class cs.cl cs.lg cs.sd eess.as energy fields gans generative hidden language language processing markov popular processing random speech type