Jun 15, 2023 · Originally designed for continuous signals, SSMs have shown superior performance on a plethora of tasks, in vision and audio; however, SSMs ...
Feb 2, 2024 · The Block-State Transformer combines State Space Models with attention, and outperforms and is more efficient over strong baselines on long sequences.
In this work, we propose a hybrid layer named Block-State Transformer (BST), that internally combines an SSM sublayer for long-range contextualization, and a ...
View recent discussion. Abstract: State space models (SSMs) have shown impressive results on tasks that require modeling long-range dependencies and ...
May 30, 2024 · Originally designed for continuous signals, SSMs have shown superior performance on a plethora of tasks, in vision and audio; however, SSMs ...
In this work, we propose a hybrid layer named Block-State Transformer (BST), that internally combines an SSM sublayer for long-range contextualization, and a ...
Implementation of Block Recurrent Transformer - Pytorch. The highlight of the paper is its reported ability to remember something up to 60k tokens ago.
Dec 14, 2023 · In addition, the Block-State Transformer demonstrates more than tenfold increase in speed at the layer level compared to the Block-Recurrent ...
Oct 3, 2024 · The Block Transformer separates the costly global modeling into the lower layers while using faster local modeling in the upper layers. The ...
