APA (7th ed.) Citation

Yang, H., Sharma, S., Liu, E., & Hostens, M. (2026). Exploring parameter-efficient fine-tuning (PEFT) of billion-parameter vision models with QLoRA and DoRA: Insights into generalization for limited-data image classification under a 98:1 test-to-train regime.

Chicago Style (17th ed.) Citation

Yang, Haiyu, Sumit Sharma, Enhong Liu, and Miel Hostens. Exploring Parameter-efficient Fine-tuning (PEFT) of Billion-parameter Vision Models with QLoRA and DoRA: Insights into Generalization for Limited-data Image Classification Under a 98:1 Test-to-train Regime. 2026.

MLA (9th ed.) Citation

Yang, Haiyu, et al. Exploring Parameter-efficient Fine-tuning (PEFT) of Billion-parameter Vision Models with QLoRA and DoRA: Insights into Generalization for Limited-data Image Classification Under a 98:1 Test-to-train Regime. 2026.