2 citations · 2 across the 8 of their papers we have counts for
6 papers · 1 filter
All You Need is One: Capsule Prompt Tuning with a Single Vector
Yiyang Liu, James C. Liang, Heng Fan +7
Prompt-based learning has emerged as a parameter-efficient finetuning (PEFT) approach to facilitate Large Language Model (LLM) adaptation to downstream tasks by conditioning genera…
Robust Ego-Exo Correspondence with Long-Term Memory
Yijun Hu, Bing Fan, Xin Gu +4
Establishing object-level correspondence between egocentric and exocentric views is essential for intelligent assistants to deliver precise and intuitive visual guidance. However,…
Probabilistic Token Alignment for Large Language Model Fusion
Runjia Zeng, James Chenhao Liang, Cheng Han +8
Training large language models (LLMs) from scratch can yield models with unique functionalities and strengths, but it is costly and often leads to redundant capabilities. A more co…
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-…