Negative Sample

Negative sampling is a crucial technique in machine learning that involves selecting data points to represent the absence of a particular feature or class, improving model training and performance. Current research focuses on developing strategies for selecting high-quality "hard" negative samples—those that are most similar to positive samples—within various model architectures, including contrastive learning frameworks and graph neural networks, often employing techniques like synthetic data generation or sophisticated sampling algorithms. Effective negative sampling is vital for enhancing model accuracy and robustness across diverse applications, from image recognition and natural language processing to recommendation systems and biomedical entity linking.