EnnengYang

Awesome-Model-Merging-Methods-Theories-Applications. Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. ACM Computing Surveys, 2026.

Awesome-Forgetting-in-Deep-Learning. A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. TPAMI, 2024.

AdaMerging. AdaMerging: Adaptive Model Merging for Multi-Task Learning. ICLR, 2024.

RepresentationSurgery. Representation Surgery for Multi-Task Model Merging. ICML, 2024.

AdaTask. AdaTask: A Task-Aware Adaptive Learning Rate Approach to Multi-Task Learning. AAAI, 2023.

Efficient-WEMoE. Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging. Arxiv, 2024.

An-Efficient-Dataset-Condensation-Plugin. An Efficient Dataset Condensation Plugin and Its Application to Continual Learning. NeurIPS, 2023.

DOP. Continual Model Merging without Data: Dual Projections for Balancing Stability and Plasticity. NeurIPS, 2025.

MMFI. Multi-Scenario and Multi-Task Aware Feature Interaction for Recommendation System. TKDD, 2024.

SurgeryV2. SurgeryV2: Bridging the Gap Between Model Merging and Multi-Task Learning with Deep Representation Surgery. Arxiv, 2024.

DFGP. Data Augmented Flatness-aware Gradient Projection for Continual Learning. ICCV, 2023.

Revisiting-Flatness-aware-Optimization-in-Continual-Learning-with-Orthogonal-Gradient-Projection. Revisiting Flatness-aware Optimization in Continual Learning with Orthogonal Gradient Projection. TPAMI, 2025.

RankOne-MoE. 秩一专家混合用于多任务学习. 计算机学报, 2025. Mixture of Rank-One Experts for Multi-Task Learning. Chinese Journal of Computers, 2025.

DTMF. Discrete Trust-aware Matrix Factorization for Fast Recommendation. IJCAI, 2019.

TiCoSeRec. Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation. AAAI, 2023.

EnnengYang.