3 papers
cs.LG2026
Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure
Xingjian Wang, Qingyu Han, Xiaodong Luo +1
Deep decoder-only Transformers often replace the original Post-Norm architecture with Pre-Norm variants because Post-Norm training is highly sensitive to warmup and learning rate u…
cs.LG2026
Revisiting Weight Regularization for Low-Rank Continual Learning
Yaoyue Zheng, Yin Zhang, Joost van de Weijer +4
Continual Learning (CL) with large-scale pre-trained models (PTMs) has recently gained wide attention, shifting the focus from training from scratch to continually adapting PTMs. T…
cs.LG2025
QuadEnhancer: Leveraging Quadratic Transformations to Enhance Deep Neural Networks
Qian Chen, Linxin Yang, Akang Wang +2
The combination of linear transformations and non-linear activation functions forms the foundation of most modern deep neural networks, enabling them to approximate highly complex…