Large Scale Transformer

Large-scale Transformers are powerful deep learning models achieving state-of-the-art results across diverse tasks, but their immense computational demands drive research into efficient training and deployment strategies. Current efforts focus on optimizing architectures (e.g., multi-path designs, sparse matrix multiplication) and developing novel training algorithms (like direct feedback alignment and federated learning) to reduce resource consumption while maintaining accuracy. These advancements are crucial for broadening access to the capabilities of large Transformers and enabling their application in resource-constrained environments and diverse fields like EEG analysis and question answering.