DriftXpress accelerates generative model training with new RKHS field approach

Researchers have developed DriftXpress, a new formulation for drifting models that significantly speeds up their training process. This method approximates the drifting kernel in a low-rank feature space, maintaining the generative quality of standard drifting models while reducing computational costs during training. DriftXpress offers a more efficient approach to one-step generative modeling, pushing the boundaries of the training-inference trade-off without sacrificing inference speed. AI

IMPACT This research could lead to more efficient training of generative models, potentially reducing computational costs and accelerating development.

RANK_REASON The cluster contains a research paper detailing a new method for generative models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Ali Falahati
• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• DriftXpress
• Gotit.pub
• Hugging Face
• IArxiv
• reproducing kernel Hilbert space
• ScienceCast

• paper
• infra

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