Hankel Singular Value Regularization for Highly Compressible State Space Models
PositiveArtificial Intelligence
A recent study introduces a novel approach to enhance the compressibility of state space models used in deep neural networks. By applying Hankel singular value regularization, researchers have found a way to achieve a rapid decay of singular values, making these models easier to compress after training. This advancement is significant as it addresses a common challenge in deploying deep learning models for long-range sequence tasks, potentially leading to more efficient applications in various fields.
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