5 accepted papers
DNA-based data storage offers an attractive alternative to traditional media due to its exceptional density, durability, and sustainability. However, errors introduced across the DNA storage pipeline critically impede accurate sequence reconstruction from noisy sequencing reads. This paper addresses
Currently, pretrained models are rapidly scaling in size, which substantially increases the cost of fine-tuning them for downstream tasks. To address this challenge, parameter-efficient fine-tuning (PEFT) methods have been developed to optimize a minimal set of parameters for adaptation. While curre
Large Language Models (LLMs) have become foundational tools in natural language processing, powering a wide range of applications and research. Many studies have shown that LLMs share significant similarities with the human brain. Neuroscience research has found that a small subset of biological neu…
Federated learning often face both data and model heterogeneity, with the latter often more challenging. Architectural differences yield incompatible representation, making the knowledge-sharing carrier central to heterogeneous collaboration. Using proxy model enables distillation-based collaboratio…
Federated learning can coordinate multiple users to participate in data training while ensuring data privacy. The collaboration of multiple agents allows for a natural connection between federated learning and collective intelligence. When there are large differences in data distribution among clien…