3 papers
cs.LG2025
Achieving Deep Continual Learning via Evolution
Aojun Lu, Junchao Ke, Chunhui Ding +3
Deep neural networks, despite their remarkable success, remain fundamentally limited in their ability to perform Continual Learning (CL). While most current methods aim to enhance…
cs.LG2025
FAS: Fast ANN-SNN Conversion for Spiking Large Language Models
Long Chen, Xiaotian Song, Andy Song +3
Spiking Large Language Models have been shown as a good alternative to LLMs in various scenarios. Existing methods for creating Spiking LLMs, i.e., direct training and ANN-SNN conv…
cs.LG2025
E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing
Yuhao Zhou, Yuxin Tian, Mingjia Shi +4
The exponential growth in model sizes has significantly increased the communication burden in Federated Learning (FL). Existing methods to alleviate this burden by transmitting com…