Diagonal State Space

Diagonal State Space (DSS) models are a class of recurrent neural networks designed to efficiently process long sequences of data, addressing limitations of traditional methods like RNNs and Transformers in handling long-range dependencies. Current research focuses on developing and optimizing DSS architectures, such as variations of the S4 model, for applications in diverse fields including speech recognition, 3D object detection, and quantum state classification. The simplicity and effectiveness of DSS models, demonstrated through competitive performance on benchmark datasets, make them a significant advancement for sequence modeling tasks, offering both improved accuracy and computational efficiency.