2 citations · 2 across the 8 of their papers we have counts for
3 papers · 1 filter
MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper
Runjia Zeng, Guangyan Sun, Qifan Wang +8
Considering deep neural networks as manifold mappers, the pretrain-then-fine-tune paradigm can be interpreted as a two-stage process: pretrain establishes a broad knowledge base, a…
Re-Imagining Multimodal Instruction Tuning: A Representation View
Yiyang Liu, James Chenhao Liang, Ruixiang Tang +8
Multimodal instruction tuning has proven to be an effective strategy for achieving zero-shot generalization by fine-tuning pre-trained Large Multimodal Models (LMMs) with instructi…
KGIF: Optimizing Relation-Aware Recommendations with Knowledge Graph Information Fusion
Dong Hyun Jeon, Wenbo Sun, Houbing Herbert Song +4
While deep-learning-enabled recommender systems demonstrate strong performance benchmarks, many struggle to adapt effectively in real-world environments due to limited use of user-…