Training Data
Training data is crucial for machine learning model development, with current research focusing on improving data quality, efficiency, and mitigating biases. Active areas include generating synthetic data to address scarcity or privacy concerns, developing algorithms to optimize data selection and usage (e.g., self-paced learning, active learning), and mitigating issues like data contamination and imbalance through techniques such as data augmentation, selective parameter merging, and novel loss functions. The quality and characteristics of training data significantly impact model performance, generalization, and robustness, influencing various applications from natural language processing and image recognition to scientific computing and medical diagnosis.
Papers - Page 38
Joint Prediction and Denoising for Large-scale Multilingual Self-supervised Learning
Boosting High Resolution Image Classification with Scaling-up Transformers
Cross-Validation for Training and Testing Co-occurrence Network Inference Algorithms
Fixing the problems of deep neural networks will require better training data and learning algorithms