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cs.LG2026
When Retain Constraints Conflict: Mitigating Forget-Retain Interference in Tabular Data
Zijie Liu, Jinhao Duan, Bingqi Shang +3
Machine unlearning aims to remove the influence of designated training data while preserving model utility, but its behavior on tabular data remains underexplored. This gap is impo…
cs.LG2026
GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs
Jianing Deng, Song Wang, Dongwei Wang +4
Mixture-of-Experts Large Language Models (MoE-LLMs) achieve strong performance but incur substantial memory overhead due to massive expert parameters. Mixed-precision quantization…
cs.LG2024★ 7 cited
DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis
Pan Wang, Qiang Zhou, Yawen Wu +2
Multimodal Sentiment Analysis (MSA) leverages heterogeneous modalities, such as language, vision, and audio, to enhance the understanding of human sentiment. While existing models…