3 papers
cs.LG2025
Mixture of Group Experts for Learning Invariant Representations
Lei Kang, Jia Li, Mi Tian +1
Sparsely activated Mixture-of-Experts (MoE) models effectively increase the number of parameters while maintaining consistent computational costs per token. However, vanilla MoE mo…
cs.LG2024
Zeroth-Order Fine-Tuning of LLMs in Random Subspaces
Ziming Yu, Pan Zhou, Sike Wang +3
Fine-tuning Large Language Models (LLMs) has proven effective for a variety of downstream tasks. However, as LLMs grow in size, the memory demands for backpropagation become increa…
cs.LG2024
4-bit Shampoo for Memory-Efficient Network Training
Sike Wang, Pan Zhou, Jia Li +1
Second-order optimizers, maintaining a matrix termed a preconditioner, are superior to first-order optimizers in both theory and practice. The states forming the preconditioner and…