arXiv:2601.21025v3 Announce Type: replace-cross Abstract: 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, training EBMs remains challenging. Direct maximum likelihood is computationally pro
The continuous push for more robust and efficient generative models in AI research is driving innovation in training methodologies, leading to new approaches like the diffusive classification loss.
Improved methods for training energy-based generative models (EBMs) could unlock their full potential for advanced AI tasks beyond simple generation, impacting various applications.
The development of a more stable and computationally feasible training mechanism for EBMs could significantly broaden their practical applicability in AI.
- · AI researchers
- · Generative AI developers
- · Companies leveraging advanced AI for synthesis
Enhanced capabilities for generative AI models across image, text, and other data types.
Potentially more powerful and interpretable AI systems through better understanding of data distributions.
New avenues for AI-driven design and scientific discovery through compositional sampling and improved data generation.
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Read at arXiv cs.LG