In their work, Gu and Dao propose a novel selection mechanism designed to enhance State Space Models (SSMs). This mechanism is primarily aimed at increasing efficiency and compression through selective processing. The authors discuss how their approach draws from various existing techniques, such as gating in recurrent neural networks, while also asserting its uniqueness. They evaluate the effectiveness of the mechanism across multiple applications, including language modeling and audio generation, showing significant improvements in speed and memory usage.
Our selection mechanism is inspired by and related to concepts such as gating, hypernetworks, and data-dependence. It can also be viewed as related to "fast weights."
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