Refining Method

Refining methods encompass a broad range of techniques aimed at improving the accuracy, efficiency, and robustness of various models and algorithms across diverse fields. Current research focuses on enhancing existing models, such as transformers and convolutional neural networks, through innovative strategies like incorporating time-dependent interactions, optimizing data packing and shuffling, and integrating self-supervised learning with brain activation data. These refinements are crucial for advancing applications in areas including time series forecasting, natural language processing, image generation, speech recognition, and video processing, ultimately leading to more accurate and efficient systems.