This paper explores the advancements of Selective State Space Models (SSMs) through a selection mechanism designed to optimize performance across both discrete and continuous data modalities, including language, DNA, and audio modeling. The authors present empirical evaluations that underline the trade-offs between model effectiveness, speed, and memory requirements. Notably, while the selection mechanism improves results for discrete tasks, it can negatively impact performance in domains where traditional SSMs excel, illustrating the nuanced balance necessary in model design and application.
The selection mechanism enhances Selective State Space Models (SSMs) for discrete data types, bridging the gap between continuous and discrete modalities such as text and DNA.
Our empirical evaluations demonstrate that while selective SSMs improve performance on discrete tasks, they may underperform on data where traditional LTI SSMs excel, indicating a necessary balance.
Collection
[
|
...
]