3 papers
cs.CL2025
Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs
Yixiao Zhou, Ziyu Zhao, Dongzhou Cheng +6
Sparse Mixture-of-Experts (SMoE) architectures are widely used in large language models (LLMs) due to their computational efficiency. However, though only a few experts are activat…
cs.LG2025
On the Evolution of Federated Post-Training Large Language Models: A Model Accessibility View
Tao Guo, Junxiao Wang, Fushuo Huo +4
Federated Learning (FL) enables training models across decentralized data silos while preserving client data privacy. Recent research has explored efficient methods for post-traini…
cs.CV2025
Backdooring Self-Supervised Contrastive Learning by Noisy Alignment
Tuo Chen, Jie Gui, Minjing Dong +3
Self-supervised contrastive learning (CL) effectively learns transferable representations from unlabeled data containing images or image-text pairs but suffers vulnerability to dat…