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Accelerating Diffusion Model Training under Minimal Budgets: A Condensation-Based Perspective
Rui Huang, Shitong Shao, Zikai Zhou +6
Diffusion models have achieved remarkable performance on a wide range of generative tasks, yet training them from scratch is notoriously resource-intensive, typically requiring mil…
Blend the Separated: Mixture of Synergistic Experts for Data-Scarcity Drug-Target Interaction Prediction
Xinlong Zhai, Chunchen Wang, Ruijia Wang +7
Drug-target interaction prediction (DTI) is essential in various applications including drug discovery and clinical application. There are two perspectives of input data widely use…
LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits
Zikai Zhou, Qizheng Zhang, Hermann Kumbong +1
Fine-tuning large language models (LLMs) is increasingly costly as models scale to hundreds of billions of parameters, and even parameter-efficient fine-tuning (PEFT) methods like…
Golden Noise for Diffusion Models: A Learning Framework
Zikai Zhou, Shitong Shao, Lichen Bai +4
Text-to-image diffusion model is a popular paradigm that synthesizes personalized images by providing a text prompt and a random Gaussian noise. While people observe that some nois…
Elucidating the Design Space of Dataset Condensation
Shitong Shao, Zikai Zhou, Huanran Chen +1
Dataset condensation, a concept within data-centric learning, efficiently transfers critical attributes from an original dataset to a synthetic version, maintaining both diversity…