4 citations · 9 across the 12 of their papers we have counts for
3 papers · 1 filter
Two-Stage Regularization-Based Structured Pruning for LLMs
Mingkuan Feng, Jinyang Wu, Siyuan Liu +7
The deployment of large language models (LLMs) is largely hindered by their large number of parameters. Structural pruning has emerged as a promising solution. Prior structured pru…
DReSS: Data-driven Regularized Structured Streamlining for Large Language Models
Mingkuan Feng, Jinyang Wu, Shuai Zhang +5
Large language models (LLMs) have achieved significant progress across various domains, but their increasing scale results in high computational and memory costs. Recent studies ha…
FLGo: A Fully Customizable Federated Learning Platform
Zheng Wang, Xiaoliang Fan, Zhaopeng Peng +6
Federated learning (FL) has found numerous applications in healthcare, finance, and IoT scenarios. Many existing FL frameworks offer a range of benchmarks to evaluate the performan…