An algorithm exploits the near-Sylvester structure of meeting time equations to compute all pairwise expected meeting times on graphs in O(N^4) operations.
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Inf-SSM constrains the infinite-horizon evolution of SSMs via Grassmannian geometry and an efficient O(n^2) Sylvester solver to enable exemplar-free continual learning with reduced forgetting.
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Meeting times on graphs in near-cubic time
An algorithm exploits the near-Sylvester structure of meeting time equations to compute all pairwise expected meeting times on graphs in O(N^4) operations.
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Exemplar-Free Continual Learning for State Space Models
Inf-SSM constrains the infinite-horizon evolution of SSMs via Grassmannian geometry and an efficient O(n^2) Sylvester solver to enable exemplar-free continual learning with reduced forgetting.