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Embedding tables are among the largest components of modern language models. Most compression methods fix a coding geometry such as coordinate blocks, low-rank subspaces, or unrestricted codebooks, and optimize within it. We instead ask whether the coding geometry can itself be discovered. We introduce \emph{OrBIT}, a...
This work introduces a computationally efficient unlearning framework that identifies correlated data points in the training set and applies a theoretically derived closed-form parameter update rule, achieving an $82\times$ wall-clock speedup over standard influence function unlearning while preserving model utility wi...
GenQAS is introduced, a tensor network-guided RL framework that combines a fixed matrix product state warm-start with prioritized generative replay and can mitigate sample starvation in quantum architecture search and support more resource efficient circuit discovery.
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