Parameter competition balancing for model merging

G Du, J Lee, J Li, R Jiang, Y Guo, S Yu, H Liu… - arxiv preprint arxiv …, 2024 - arxiv.org
While fine-tuning pretrained models has become common practice, these models often
underperform outside their specific domains. Recently developed model merging …

Impacts of darwinian evolution on pre-trained deep neural networks

G Du, R Jiang, S Yang, H Li, W Chen… - … on Systems, Man …, 2024 - ieeexplore.ieee.org
Darwinian evolution of the biological brain is documented through multiple lines of
evidence, although the modes of evolutionary changes remain unclear. Drawing inspiration …

How Far Are We From AGI: Are LLMs All We Need?

T Feng, C **, J Liu, K Zhu, H Tu, Z Cheng… - … on Machine Learning …, 2024 - openreview.net
The evolution of artificial intelligence (AI) has profoundly impacted human society, driving
significant advancements in multiple sectors. Yet, the escalating demands on AI have …

Evolutionary neural architecture search for 3d point cloud analysis

Y Yang, G Du, CK Toa, HK Tang… - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
Neural architecture search (NAS) automates neural network design by using optimization
algorithms to navigate architecture spaces, reducing the burden of manual architecture …

Meta-heuristic optimizer inspired by the philosophy of yi **g

Y Yang, SK Goh, Q Cai, SY Wong, HK Tang - arxiv preprint arxiv …, 2024 - arxiv.org
Drawing inspiration from the philosophy of Yi **g, the Yin-Yang pair optimization (YYPO)
algorithm has been shown to achieve competitive performance in single objective …

Parameter Competition Balancing for Model Merging

DU Guodong, J Lee, J Li, R Jiang, Y Guo, S Yu… - The Thirty-eighth Annual … - openreview.net
While fine-tuning pretrained models has become common practice, these models often
underperform outside their specific domains. Recently developed model merging …

LOCMAP: LOW-COMPUTE MODEL MERGING WITH AMORTIZED PARETO FRONTS VIA QUADRATIC APPROXIMATION

AP FRONTS - openreview.net
Model merging has emerged as an effective approach to combine multiple single-task
models into a multitask model. This process typically involves computing a weighted …