Energy-based generator matching: A neural sampler for general state space
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909912807768064 |
|---|---|
| author | Woo, Dongyeop Kim, Minsu Kim, Minkyu Seong, Kiyoung Ahn, Sungsoo |
| author_facet | Woo, Dongyeop Kim, Minsu Kim, Minkyu Seong, Kiyoung Ahn, Sungsoo |
| contents | 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 proposed generator matching, EGM enables training of arbitrary continuous-time Markov processes, e.g., diffusion, flow, and jump, and can generate data from continuous, discrete, and a mixture of two modalities. To this end, we propose estimating the generator matching loss using self-normalized importance sampling with an additional bootstrapping trick to reduce variance in the importance weight. We validate EGM on both discrete and multimodal tasks up to 100 and 20 dimensions, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19646 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Energy-based generator matching: A neural sampler for general state space Woo, Dongyeop Kim, Minsu Kim, Minkyu Seong, Kiyoung Ahn, Sungsoo Machine Learning 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 proposed generator matching, EGM enables training of arbitrary continuous-time Markov processes, e.g., diffusion, flow, and jump, and can generate data from continuous, discrete, and a mixture of two modalities. To this end, we propose estimating the generator matching loss using self-normalized importance sampling with an additional bootstrapping trick to reduce variance in the importance weight. We validate EGM on both discrete and multimodal tasks up to 100 and 20 dimensions, respectively. |
| title | Energy-based generator matching: A neural sampler for general state space |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.19646 |