3 papers
cs.LG2026
Riemannian MeanFlow
Dongyeop Woo, Marta Skreta, Seonghyun Park +2
Diffusion and flow models have become the dominant paradigm for generative modeling on Riemannian manifolds, with successful applications in protein backbone generation and DNA seq…
cs.LG2025
Energy-based generator matching: A neural sampler for general state space
Dongyeop Woo, Minsu Kim, Minkyu Kim +2
We propose Energy-based generator matching (EGM), a modality-agnostic approach to train generative models from energy functions in the absence of data. Extending the recently propo…
cs.LG2025
On scalable and efficient training of diffusion samplers
Minkyu Kim, Kiyoung Seong, Dongyeop Woo +2
We address the challenge of training diffusion models to sample from unnormalized energy distributions in the absence of data, the so-called diffusion samplers. Although these appr…