1 citations · 1 across the 2 of their papers we have counts for
4 papers
PARA: Parameter-Efficient Fine-tuning with Prompt Aware Representation Adjustment
Zequan Liu, Yi Zhao, Ming Tan +2
In the realm of parameter-efficient fine-tuning (PEFT) methods, while options like LoRA are available, there is a persistent demand in the industry for a PEFT approach that excels…
IAPT: Instruction-Aware Prompt Tuning for Large Language Models
Wei Zhu, Aaron Xuxiang Tian, Congrui Yin +3
Soft prompt tuning is a widely studied parameter-efficient fine-tuning method. However, it has a clear drawback: many soft tokens must be inserted into the input sequences to guara…
NGM-SLAM: Gaussian Splatting SLAM with Radiance Field Submap
Jingwei Huang, Mingrui Li, Lei Sun +3
SLAM systems based on Gaussian Splatting have garnered attention due to their capabilities for rapid real-time rendering and high-fidelity mapping. However, current Gaussian Splatt…
WcDT: World-centric Diffusion Transformer for Traffic Scene Generation
Chen Yang, Yangfan He, Aaron Xuxiang Tian +5
In this paper, we introduce a novel approach for autonomous driving trajectory generation by harnessing the complementary strengths of diffusion probabilistic models (a.k.a., diffu…