68 citations · 110 across the 13 of their papers we have counts for
4 papers · 1 filter
SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models
Anke Tang, Li Shen, Yong Luo +5
Deep model training on extensive datasets is increasingly becoming cost-prohibitive, prompting the widespread adoption of deep model fusion techniques to leverage knowledge from pr…
Towards Efficient Pareto Set Approximation via Mixture of Experts Based Model Fusion
Anke Tang, Li Shen, Yong Luo +4
Solving multi-objective optimization problems for large deep neural networks is a challenging task due to the complexity of the loss landscape and the expensive computational cost…
FusionBench: A Unified Library and Comprehensive Benchmark for Deep Model Fusion
Anke Tang, Li Shen, Yong Luo +5
Deep model fusion is an emerging technique that unifies the predictions or parameters of several deep neural networks into a single better-performing model in a cost-effective and…
WisdoM: Improving Multimodal Sentiment Analysis by Fusing Contextual World Knowledge
Wenbin Wang, Liang Ding, Li Shen +3
Sentiment analysis is rapidly advancing by utilizing various data modalities (e.g., text, image). However, most previous works relied on superficial information, neglecting the inc…