Dimension d = O(m^{-2} log n) nearly achieves the optimal margin m^rd(+∞, A) for retrieval embeddings, with matching lower bounds showing d = O(k log(n/k)) suffices and is necessary for m = Θ(k^{-1/2}) on k-sparse query matrices.
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Some complexity questions related to distributive computing(preliminary re- port)
3 Pith papers cite this work, alongside 1,067 external citations. Polarity classification is still indexing.
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Dense network coding computes group operations over multiaccess networks with half the classical communication cost using shared entanglement plus quantum channels, and yields measurement-device-independent quantum key growing.
Greedy vector balancing on finite unit-vector sets T in R^d achieves norm bound (2/δ_T)^{d-1} independent of sequence length n.
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Is Dimensionality a Barrier for Retrieval Models?
Dimension d = O(m^{-2} log n) nearly achieves the optimal margin m^rd(+∞, A) for retrieval embeddings, with matching lower bounds showing d = O(k log(n/k)) suffices and is necessary for m = Θ(k^{-1/2}) on k-sparse query matrices.
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Communication Advantages from Quantum Dense Network Coding
Dense network coding computes group operations over multiaccess networks with half the classical communication cost using shared entanglement plus quantum channels, and yields measurement-device-independent quantum key growing.
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Greedy Vector Balancing
Greedy vector balancing on finite unit-vector sets T in R^d achieves norm bound (2/δ_T)^{d-1} independent of sequence length n.