Training Signal

Training signals are the data or information used to guide the learning process in machine learning models, impacting their accuracy and efficiency. Current research focuses on improving training signals by addressing issues like noisy labels, exploring the dynamics of signal propagation within models (e.g., using geometric analysis of transformer architectures), and developing more effective strategies for leveraging both labeled and unlabeled data (e.g., through semi-supervised and self-supervised learning). These advancements are crucial for enhancing model performance across various tasks, including natural language processing, computer vision, and other areas where robust and efficient training is essential.