5 papers · 1 filter
MOSH-WM: Mask-Grounded Soft-Hamiltonian Dynamics for Object-Centric World Models
Zhekai Wang, Haoxiang Huang, Xiang Liu +6
Object-centric world models forecast future videos by evolving a set of entity slots, but the variables receiving dynamics supervision are often unconstrained visual features. We i…
From Coefficients to Directions: Rethinking Model Merging with Directional Alignment
Zhikang Chen, Sen Cui, Deheng Ye +5
Model merging has emerged as a practical paradigm for integrating multiple independently trained models into a single model without joint retraining. Previous studies have demonstr…
Merging without Forgetting: Continual Fusion of Task-Specific Models via Optimal Transport
Zecheng Pan, Zhikang Chen, Ding Li +9
Merging models fine-tuned for different tasks into a single unified model has become an increasingly important direction for building versatile, efficient multi-task systems. Exist…
Decentralized Dynamic Cooperation of Personalized Models for Federated Continual Learning
Danni Yang, Zhikang Chen, Sen Cui +6
Federated continual learning (FCL) has garnered increasing attention for its ability to support distributed computation in environments with evolving data distributions. However, t…
Learning without Isolation: Pathway Protection for Continual Learning
Zhikang Chen, Abudukelimu Wuerkaixi, Sen Cui +10
Deep networks are prone to catastrophic forgetting during sequential task learning, i.e., losing the knowledge about old tasks upon learning new tasks. To this end, continual learn…