Abstract Low-Rank Adaptation (LoRA), which updates the dense neural network layers with
pluggable low-rank matrices, is one of the best performed parameter efficient fine-tuning …

We introduce LDAdam, a memory-efficient optimizer for training large models, that performs
adaptive optimization steps within lower dimensional subspaces, while consistently …

Artificial intelligence (AI) methods are poised to revolutionize intellectual work, with
generative AI enabling automation of text analysis, text generation, and simple decision …

Low-rank adaptation of large models, particularly LoRA, has gained traction due to its
computational efficiency. This efficiency, contrasted with the prohibitive costs of full-model …

As gravitational wave detectors become more advanced and sensitive, the number of
signals recorded by Advanced LIGO and Virgo from merging compact objects is expected to …

Large neural networks achieve remarkable performance, but their size hinders deployment
on resource-constrained devices. While various compression techniques exist, parameter …

Abstract While Large Vision–Language Models (LVLMs) like GPT-4 and Gemini
demonstrate significant potential, their utilization in the medical domain remains largely …

Fine-tuning large language models (LLMs) for downstream tasks has become increasingly
crucial due to their widespread use and the growing availability of open-source models …

The integration of human and artificial intelligence represents a scientific opportunity to
advance our understanding of information processing, as each system offers unique …

Low-rank adaptation (LoRA) has emerged as a powerful method for fine-tuning large-scale
foundation models. Despite its popularity, the theoretical understanding of LoRA has …