Predictor Corrector

Predictor-corrector methods are iterative algorithms that enhance the accuracy and efficiency of various computational tasks by combining a prediction step with a subsequent correction step. Current research focuses on applying these methods to improve the training and sampling of deep learning models, particularly in areas like dense retrieval and diffusion models, often employing techniques like corrector networks to adjust stale embeddings or refine sampling processes. This approach is proving valuable for mitigating computational costs associated with large datasets and complex models, leading to improvements in efficiency and accuracy across diverse applications, including natural language processing and image generation.