# 苹果提出摊销MIPS方法：用神经网络直接预测最大内积搜索解

- 来源：Apple Machine Learning Research（RSS）
- 发布时间：2026-07-02 08:00
- AIHOT 分数：51
- AIHOT 链接：https://aihot.virxact.com/items/cmr3rd0l901bvsl7lc4iiyd2r
- 原文链接：https://machinelearning.apple.com/research/amortizing-inner-product-search

## AI 摘要

苹果机器学习研究团队提出摊销MIPS方法，训练神经网络直接预测最大内积搜索（MIPS）的解。核心思路是将MIPS值函数建模为键集的凸支撑函数，其梯度指向最优键。据此设计两种互补模型：SupportNet（输入凸神经网络拟合支撑函数，用作聚类路由）和KeyNet（向量值网络直接回归最优键，可替换原始查询输入索引流水线）。在BEIR基准文档嵌入实验中，两种模型在FLOPs、探测次数或时钟时间等计算开销指标下均显著提升IVF匹配率。代码已开源。

## 正文

research area Knowledge Bases and Search, research area Methods and Algorithmsconference ICML

content type paperpublished July 2026

Amortizing Maximum Inner Product Search with Learned Support Functions

AuthorsTheo X. Olausson†**, João Monteiro, Michal Klein, Marco Cuturi

View publication

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 support function of the set of keys, a well-studied convex function whose gradient yields the optimal key. This motivates two complementary amortized models: SupportNet, an input-convex neural network trained to regress the support function, and KeyNet, a vector-valued network that directly regresses the optimal key. SupportNet can serve as a cluster router, steering queries toward relevant database partitions, while KeyNet can be used as a drop-in replacement for the original query, fed directly to off-the-shelf indexing pipelines. Our experiments on the BEIR benchmark show that, for document embeddings, learned SupportNets and KeyNets significantly improve IVF match rates when accounting for compute effort, whether measured in FLOPs, number of probes, or wall-clock time. Our code is available at: https://github.com/apple/ml-amips.

† MIT

** Work done while at Apple

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