2 citations · 3 across the 5 of their papers we have counts for
5 papers
Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models
Longteng Zhang, Sen Wu, Shuai Hou +7
Adapting large pre-trained language models to downstream tasks often entails fine-tuning millions of parameters or deploying costly dense weight updates, which hinders their use in…
PipeDiT: Accelerating Diffusion Transformers in Video Generation with Task Pipelining and Model Decoupling
Sijie Wang, Qiang Wang, Shaohuai Shi
Video generation has been advancing rapidly, and diffusion transformer (DiT) based models have demonstrated remark- able capabilities. However, their practical deployment is of- te…
HybridEP: Scaling Expert Parallelism to Cross-Datacenter Scenario via Hybrid Expert/Data Transmission
Weihao Yang, Hao Huang, Donglei Wu +6
Mixture-of-Experts (MoE) has become a popular architecture for scaling large models. However, the rapidly growing scale outpaces model training on a single DC, driving a shift towa…
From Leakage to Fidelity: Reliable Benchmarking for Temporal Cascade Prediction
Jie Peng, Rui Wang, Qiang Wang +4
Temporal cascade prediction is widely studied, yet its empirical foundations remain fragile. Most existing works report results under random cascade splits that mix past and future…
CPRM: A LLM-based Continual Pre-training Framework for Relevance Modeling in Commercial Search
Kaixin Wu, Yixin Ji, Zeyuan Chen +9
Relevance modeling between queries and items stands as a pivotal component in commercial search engines, directly affecting the user experience. Given the remarkable achievements o…