Data Based Curriculum

Data-based curriculum learning aims to improve the efficiency and effectiveness of training machine learning models by carefully sequencing the order in which data is presented, mimicking how humans learn progressively. Current research explores various methods for ordering data, including those based on estimated sample difficulty, task complexity, and model competence, often employing algorithms like simulated annealing or leveraging learning rate adjustments across neural network layers. While some studies demonstrate performance improvements using these curricula, others find that randomly ordered data performs comparably or even better, highlighting the need for more robust and universally applicable data ordering strategies. This research area is significant because efficient training of large models is crucial for advancing machine learning applications across diverse fields.