핵심 개념
Mamba models can be viewed as attention-driven models, shedding light on their inner workings and comparison to transformers.
초록
Mamba models offer efficient state space modeling for various domains like NLP, vision, and more. They can be seen as attention-driven models, enabling explainability methods for interpretation. The research aims to provide insights into the dynamics of Mamba models and develop methodologies for their interpretation. By reformulating Mamba computation using a data-control linear operator, hidden attention matrices within the Mamba layer are unveiled. This allows for well-established interpretability techniques commonly used in transformer realms to be applied to Mamba models.
통계
Mamba models offer a 5x increase in throughput compared to Transformers.
Selective SSMs have shown remarkable performance in language modeling, image processing, video processing, medical imaging, tabular data analysis, point-cloud analysis, graphs, and N-dimensional sequence modeling.
The Mamba block is built on top of the selective state-space layer Conv1D and other elementwise operators.
인용구
"Mamba layers offer an efficient selective state space model that is highly effective in multiple domains including NLP and computer vision."
"Our main contributions shed light on the fundamental nature of Mamba models by showing that they rely on implicit attention."