Toward theoretical insights into diffusion trajectory distillation via operator merging.
- DOI
- 10.1016/j.neunet.2026.109023
- Published
- 2026 Oct
- Container
- Neural networks : the official journal of the International Neural Network Society
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2026.109023,
title = {Toward theoretical insights into diffusion trajectory distillation via operator merging.},
author = {Gao W and Li M},
year = {2026},
journal = {Neural networks : the official journal of the International Neural Network Society},
doi = {10.1016/j.neunet.2026.109023},
url = {https://doi.org/10.1016/j.neunet.2026.109023}
}RIS
TY - JOUR TI - Toward theoretical insights into diffusion trajectory distillation via operator merging. AU - Gao W AU - Li M PY - 2026 JO - Neural networks : the official journal of the International Neural Network Society DO - 10.1016/j.neunet.2026.109023 UR - https://doi.org/10.1016/j.neunet.2026.109023 ER -
APA
W, G., & M, L. (2026). Toward theoretical insights into diffusion trajectory distillation via operator merging.. Neural networks : the official journal of the International Neural Network Society. https://doi.org/10.1016/j.neunet.2026.109023
Source records
- pubmed · retrieved 2026-09-25T22:51:24.794Z