409 citations · 677 across the 54 of their papers we have counts for
7 papers · 1 filter
Copycat vs. Original: Multi-modal Pretraining and Variable Importance in Box-office Prediction
Qin Chao, Eunsoo Kim, Boyang Li
The movie industry is associated with an elevated level of risk, which necessitates the use of automated tools to predict box-office revenue and facilitate human decision-making. I…
GELD: A Unified Neural Model for Efficiently Solving Traveling Salesman Problems Across Different Scales
Yubin Xiao, Di Wang, Rui Cao +3
The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem with broad real-world applications. Recent advancements in neural network-based TSP solvers…
CAT Merging: A Training-Free Approach for Resolving Conflicts in Model Merging
Wenju Sun, Qingyong Li, Yangli-ao Geng +1
Multi-task model merging offers a promising paradigm for integrating multiple expert models into a unified model without additional training. Existing state-of-the-art techniques,…
Towards Minimizing Feature Drift in Model Merging: Layer-wise Task Vector Fusion for Adaptive Knowledge Integration
Wenju Sun, Qingyong Li, Wen Wang +3
Multi-task model merging aims to consolidate knowledge from multiple fine-tuned task-specific experts into a unified model while minimizing performance degradation. Existing method…
Enhancing Vision-Language Compositional Understanding with Multimodal Synthetic Data
Haoxin Li, Boyang Li
Paired image-text data with subtle variations in-between (e.g., people holding surfboards vs. people holding shovels) hold the promise of producing Vision-Language Models with prop…
Learning to Animate Images from A Few Videos to Portray Delicate Human Actions
Haoxin Li, Yingchen Yu, Qilong Wu +3
Despite recent progress, video generative models still struggle to animate static images into videos that portray delicate human actions, particularly when handling uncommon or nov…
Conversational Explanations: Discussing Explainable AI with Non-AI Experts
Tong Zhang, Mengao Zhang, Wei Yan Low +2
Explainable AI (XAI) aims to provide insights into the decisions made by AI models. To date, most XAI approaches provide only one-time, static explanations, which cannot cater to u…