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EnnengYang/README.md

Hi 👋

My research interests lie in large language models, machine learning, and recommender systems. More specifically, I focus on:

  • Large Language Model: continual pretraining/finetuning, knowledge editing
  • Machine Learning: model merging, multi-task learning, continual/incremental learning, data-free learning, dataset/knowledge distillation
  • Recommendation System: multi-task/multi-scenario recommendation, sequential recommendation, OOD recommendation

🔥🔥🔥 Our team is seeking self-motivated students (including remote internships, undergraduates, graduate students, and other candidates) to join research on LLMs, continual learning, and model merging, with the goal of publishing high-quality academic papers. If interested, please email me your resume.

💬 Contacts: ennengyang@qq.com / ennengyang@gmail.com

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  1. Awesome-Model-Merging-Methods-Theories-Applications Awesome-Model-Merging-Methods-Theories-Applications Public

    Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. ACM Computing Surveys, 2025.

    661 37

  2. Awesome-Forgetting-in-Deep-Learning Awesome-Forgetting-in-Deep-Learning Public

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

    349 17

  3. AdaMerging AdaMerging Public

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

    Python 99 5

  4. RepresentationSurgery RepresentationSurgery Public

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

    Python 47 4

  5. AdaTask AdaTask Public

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

    Python 29 1

  6. An-Efficient-Dataset-Condensation-Plugin An-Efficient-Dataset-Condensation-Plugin Public

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

    Python 12