Reformer (efficient transformer)
Reformer is a transformer model architecture designed to improve the efficiency of attention mechanisms in deep learning by reducing memory and computational costs. It introduces techniques such as locality-sensitive hashing and reversible layers to enable the processing of longer sequences.