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.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , month =
2 Pith papers cite this work. Polarity classification is still indexing.
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DIVE proposes a dimensionality-reduction adapter using self-limiting gradients and implicit view ensembles that outperforms prior adapters on all six BEIR datasets at every tested compression ratio.
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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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DIVE: Embedding Compression via Self-Limiting Gradient Updates
DIVE proposes a dimensionality-reduction adapter using self-limiting gradients and implicit view ensembles that outperforms prior adapters on all six BEIR datasets at every tested compression ratio.