6 papers · 1 filter
Scaling Parameter-Efficient Fine-Tuning for Brain Encoding
Zhuofan Josh Ying, Guanglong Sun, Nolan Ho +6
Brain encoding models aim to predict neural responses to stimuli. The most widely used approach fits readouts from frozen pretrained backbones. Using neural data to train large mod…
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…