3 papers
cs.LG2025
HEAPr: Hessian-based Efficient Atomic Expert Pruning in Output Space
Ke Li, Zheng Yang, Zhongbin Zhou +3
Mixture-of-Experts (MoE) architectures in large language models (LLMs) deliver exceptional performance and reduced inference costs compared to dense LLMs. However, their large para…
cs.CV2025
HieraEdgeNet: A Multi-Scale Edge-Enhanced Framework for Automated Pollen Recognition
Yuchong Long, Wen Sun, Ningxiao Sun +3
Automated pollen recognition is vital to paleoclimatology, biodiversity monitoring, and public health, yet conventional methods are hampered by inefficiency and subjectivity. Exist…
cs.CL2023
To be or not to be? an exploration of continuously controllable prompt engineering
Yuhan Sun, Mukai Li, Yixin Cao +4
As the use of large language models becomes more widespread, techniques like parameter-efficient fine-tuning and other methods for controlled generation are gaining traction for cu…