Kernel Affine Hull Machines as Compute-Efficient Encoders for Frozen Semantic Spaces
Transformer-based semantic encoders are effective for retrieval, but in many deployments the recurring bottleneck is online query encoding rather than offline corpus indexing. This paper studies whether, once a strong teacher representation space and corpus index are fixed, repeated neural query encoding can be replaced by a substantially lighter and analytically explicit estimator. We formulate fixed-teacher lexical-to-semantic encoding as a conditional-mean estimation problem in which the target semantic vector is represented as a noisy mixture of semantic prototypes weighted by posterior cluster probabilities. Kernel Affine Hull Machine (KAHM) geometry is used to estimate these posteri...
| Authors | Mohit Kumar, Somayeh Kargaran, Bernhard A. Moser et al. |
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| Primary category | cs.LG |
| Published | 1 May 2026 |
| Last updated at source | 6 June 2026 |
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