Training Sample

Training sample selection and manipulation are crucial for optimizing machine learning model performance and addressing various challenges, including data scarcity, bias, and generalization. Current research focuses on developing strategies for selecting informative samples, generating synthetic data to augment existing datasets, and mitigating the negative impacts of memorization and distribution shifts. These efforts aim to improve model accuracy, efficiency, and fairness across diverse applications, from natural language processing and speech recognition to medical image analysis and audio processing. The ultimate goal is to develop more robust and reliable models that generalize well to unseen data and avoid undesirable biases.