Energy-Based Models
energy-based-models-864311da·2 events·first seen 28d agoAliases: Energy-Based Models, Energy-Based Model
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Implicit Generation and Generalization Methods for Energy-Based Models
OpenAI published research on stable and scalable training of energy-based models (EBMs), achieving sample quality competitive with GANs at low temperatures while retaining mode coverage guarantees of likelihood-based models. The approach uses iterative compute during generation to continually refine outputs. This work positions EBMs as a promising alternative generative modeling paradigm bridging GANs and likelihood-based models.
Learning Concepts with Energy Functions
OpenAI presents an energy-based model capable of learning abstract spatial concepts—such as 'near,' 'above,' and 'between'—from only five demonstrations using sets of 2D points. The model generalizes across domains, transferring concepts learned in a 2D particle environment to control tasks in a 3D physics-based robot simulation. The work demonstrates few-shot concept acquisition and cross-domain transfer via energy function representations.