The post discusses the Mamba model, which utilizes selective state space models (SSM) for sequence modeling. It addresses the limitations of multi-head attention in Transformers and explains how Mamba scales linearly. The post also covers the core issue with SSMs and the implementation of Mamba in Keras and TensorFlow.
Table of contents
Mamba: SSM, Theory, and Implementation in Keras and TensorFlowWhat’s so unique about Mamba?The backbone of Mamba: State Space ModelsSSM and recurrenceMamba and ‘Selective’ SSMFinal Mamba architectureTensorFlow and Keras implementationSort: