Amortized Maximum Inner Product Search with Learned Support Functions

arXiv stat.ML1mo4 min read

arXiv:2603.08001v3 Announce Type: replace-cross Abstract: Maximum inner product search (MIPS) is a crucial subroutine in machine learning, requiring the identification of a vector taken within a database (the keys) that best aligns with a given query. We propose amortized MIPS: a regression-based approach that trains neural networks to directly predict MIPS solutions, amortizing the cost of repeatedly solving MIPS for queries drawn from a known distribution over a fixed key database. Our key insight is that the MIPS value function is the \emph{support} function of the set of keys, a well-studi

Read the full story at arXiv stat.ML →

Amortized Maximum Inner Product Search with Learned Support Functions

More in Machine Learning

Reducing Human Annotation with ML Active Learning

Learning What Matters: Supervising Sparse Attention Routing with Causal Evidence Sets

On the Depth Scalability of Logic Gate Networks

Pretraining EHR Foundation Models with Patient-Aware Sampling

From Perturbation Correction to Geometry-Aware Sampling: Sharpness-Guided Equilibrium Sampling for Balanced Flat Minima in Long-Tailed Learning

Parameter-free Adaptive Sparse Attention via Compression-Based Content Selection