The article presents advancements in selective state space models (SSMs), emphasizing their role in improving efficiency for various tasks like language modeling, DNA analysis, and audio generation. The authors discuss selection as a compression mechanism and its implementation challenges, alongside empirical evaluations across synthetic and real-world datasets. Key insights are provided on the Linear Attention framework and its variants, illustrating their influence on recurrent models. Ultimately, the article emphasizes the importance of selection mechanisms in optimizing model performance and resource utilization.
The Linear Attention (LA) framework is pivotal in linking kernel attention with recurrent autoregressive models, showcasing various kernel modifications and approximations.
Random Feature Attention approximates softmax through random Fourier features, enabling efficient kernel attention, while Performer enhances the exponential kernel for better stability.
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