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ScaleOT Framework Unveiled at AAAI 2025: 50% Privacy Enhancement, 90% Reduced Computational Cost

A collaborative team from Ant Group, Zhejiang University, University of Liverpool, and East China Normal University presented ScaleOT, a novel cross-domain fine-tuning framework, at AAAI 2025. This framework achieves a 50% improvement in privacy protection and a remarkable 90% reduction in computational cost compared to knowledge distillation, without compromising model performance. ScaleOT offers a highly efficient and lightweight solution for cross-domain fine-tuning of billion-parameter models. The innovative nature of the paper secured it an oral presentation at AAAI.

9.6k 1 days ago
ScaleOT Framework Unveiled at AAAI 2025: 50% Privacy Enhancement, 90% Reduced Computational Cost

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Kairos_10m

mldi-lab

K

Kairos-10M is a time series foundation model designed for cross-domain zero-shot prediction, with approximately 10 million parameters. It can handle heterogeneous time series data with different information densities and achieve strong generalization ability across different domains without fine-tuning.

Scientific ComputingTransformersTransformers
mldi-lab
252
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