5 papers
Neural Parameter Search for Slimmer Fine-Tuned Models and Better Transfer
Guodong Du, Zitao Fang, Jing Li +10
Foundation models and their checkpoints have significantly advanced deep learning, boosting performance across various applications. However, fine-tuned models often struggle outsi…
Parameter Competition Balancing for Model Merging
Guodong Du, Junlin Lee, Jing Li +8
While fine-tuning pretrained models has become common practice, these models often underperform outside their specific domains. Recently developed model merging techniques enable t…
Impacts of Darwinian Evolution on Pre-trained Deep Neural Networks
Guodong Du, Runhua Jiang, Senqiao Yang +5
Darwinian evolution of the biological brain is documented through multiple lines of evidence, although the modes of evolutionary changes remain unclear. Drawing inspiration from th…
Knowledge Fusion By Evolving Weights of Language Models
Guodong Du, Jing Li, Hanting Liu +5
Fine-tuning pre-trained language models, particularly large language models, demands extensive computing resources and can result in varying performance outcomes across different d…
CADE: Cosine Annealing Differential Evolution for Spiking Neural Network
Runhua Jiang, Guodong Du, Shuyang Yu +3
Spiking neural networks (SNNs) have gained prominence for their potential in neuromorphic computing and energy-efficient artificial intelligence, yet optimizing them remains a form…