Researchers have introduced a new approach to sequence modeling called linear oscillatory state-space (LinOSS) models, designed for efficient learning on long sequences. Drawing inspiration from ...

MIT CSAIL researchers have introduced linear oscillatory state-space models (LinOSS), inspired by neural oscillations in the brain, to enhance machine learning’s ability to handle long data sequences.

ABSTRACT: The VMamba (Visual State Space Model) is built upon the Mamba model by stacking Visual State Space (VSS) modules and utilizing the 2D Selective Scan (SS2D) module to extend the original ...

State Space Models belong to the group of sequence models with linear complexity with respect to the length of the input sequence. The architecture of State Space Models aligns more with Recurrent ...

A team of researchers has introduced Graph-Mamba, an innovative model integrating a selective SSM into the GraphGPS framework. It presents an efficient solution to input-dependent graph sparsification ...

In this article on Mamba, we'll explore how this innovative state-space model (SSM) revolutionizes sequence modeling. Developed by Albert Gu and Tri Dao, Mamba is distinguished for its efficiency in ...