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20232025
most citedHierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality

18 citations · 49 across the 10 of their papers we have counts for

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cs.LG2024

HiDe-PET: Continual Learning via Hierarchical Decomposition of Parameter-Efficient Tuning

Liyuan Wang, Jingyi Xie, Xingxing Zhang +2

The deployment of pre-trained models (PTMs) has greatly advanced the field of continual learning (CL), enabling positive knowledge transfer and resilience to catastrophic forgettin…

cs.LG2024

Orchestrate Latent Expertise: Advancing Online Continual Learning with Multi-Level Supervision and Reverse Self-Distillation

HongWei Yan, Liyuan Wang, Kaisheng Ma +1

To accommodate real-world dynamics, artificial intelligence systems need to cope with sequentially arriving content in an online manner. Beyond regular Continual Learning (CL) atte…

cs.LG2023★ 3 cited

Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation

Yilin Lyu, Liyuan Wang, Xingxing Zhang +4

Continual learning entails learning a sequence of tasks and balancing their knowledge appropriately. With limited access to old training samples, much of the current work in deep n…

cs.LG2023★ 18 cited

Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality

Liyuan Wang, Jingyi Xie, Xingxing Zhang +3

Prompt-based continual learning is an emerging direction in leveraging pre-trained knowledge for downstream continual learning, and has almost reached the performance pinnacle unde…

cs.LG2023★ 1 cited

Towards a General Framework for Continual Learning with Pre-training

Liyuan Wang, Jingyi Xie, Xingxing Zhang +2

In this work, we present a general framework for continual learning of sequentially arrived tasks with the use of pre-training, which has emerged as a promising direction for artif…