12 citations · 13 across the 14 of their papers we have counts for
7 papers · 1 filter
Brain-Inspired Hierarchical Modularity for General Continual Learning
Hongwei Yan, Kanglei Zhou, Qi Cheng +7
Continual learning, the ability to learn from sequential experience while retaining and adapting prior knowledge, is central to intelligent systems operating in changing environmen…
FlyPrompt: Brain-Inspired Random-Expanded Routing with Temporal-Ensemble Experts for General Continual Learning
Hongwei Yan, Guanglong Sun, Kanglei Zhou +3
General continual learning (GCL) challenges intelligent systems to learn from single-pass, non-stationary data streams without clear task boundaries. While recent advances in conti…
Safety Alignment as Continual Learning: Mitigating the Alignment Tax via Orthogonal Gradient Projection
Guanglong Sun, Siyuan Zhang, Liyuan Wang +3
Safety post-training can improve the harmfulness and policy compliance of Large Language Models (LLMs), but it may also reduce general utility, a phenomenon often described as the…
Domain Generalizable Continual Learning
Hongwei Yan, Guanglong Sun, Zhiqi Kang +2
To adapt effectively to dynamic real-world environments, intelligent systems must continually acquire new skills while generalizing them to diverse, unseen scenarios. Here, we intr…
Right Time to Learn:Promoting Generalization via Bio-inspired Spacing Effect in Knowledge Distillation
Guanglong Sun, Hongwei Yan, Liyuan Wang +3
Knowledge distillation (KD) is a powerful strategy for training deep neural networks (DNNs). Although it was originally proposed to train a more compact "student" model from a larg…
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…
Diffusion Models for Reinforcement Learning: A Survey
Zhengbang Zhu, Hanye Zhao, Haoran He +5
Diffusion models surpass previous generative models in sample quality and training stability. Recent works have shown the advantages of diffusion models in improving reinforcement…