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arXiv Machine Learning By Jyothish Pari, Ryan Bahlous-Boldi, Pulkit Agrawal

Dynamic Compression in Recurrent Networks

Read the original on arXiv Machine Learning →

Dynamic Compression in Recurrent Networks proposes a method that lets recurrent models revisit and revise their fixed-size state through additional updates, rather than compressing all information in a single causal pass. This approach allows the model to retain lower-fidelity history and refine only the relevant parts when needed, reducing the required state size for accurate task reuse. Experiments show that dynamic compression lowers the recurrent state needed and scales better as the number of stored functions increases.

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