A Data-Centric Approach to improve performance of deep learning models
Whang, S. E., Roh, Y., Song, H. & Lee, J. G. Data collection and quality challenges in deep learning: a...
Performance
DPAdapter: A New Technique Designed to Amplify the Model Performance of Differentially Private Machine Learning DPML Algorithms by Enhancing Parameter Robustness
Privacy in machine learning is critical, especially when models are trained on sensitive data. Differential privacy (DP) offers a framework...
E-learning platforms in ideological and political education at universities: students’ motivation and learning performance | BMC Medical Education
Using e-learning platforms as a promising direction for developing the education systemTeaching and learning in an online environment ensured the...
LoRA-Pro: A Groundbreaking Machine Learning Approach to Bridging the Performance Gap Between Low-Rank Adaptation and Full Fine-Tuning
Parameter-efficient fine-tuning (PEFT) methods have become essential in machine learning. They allow large models to adapt to new tasks without...
