arXiv:2603.08001v2 Announce Type: replace 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-studied con
The increasing scale and complexity of machine learning models necessitate more efficient methods for fundamental operations like MIPS, driving innovation in amortized, learned approaches.
This development proposes a significant optimization for a core machine learning subroutine, potentially enhancing the efficiency of large-scale AI systems across various applications.
Machine learning systems can now use neural networks to directly and more efficiently predict MIPS solutions for frequently queried databases, reducing computational costs and improving inference speed.
- · AI model developers
- · Cloud computing providers
- · Companies with large recommendation systems
- · Machine learning researchers
- · Inefficient MIPS algorithms
- · Systems heavily reliant on traditional MIPS implementations
Faster and more cost-effective deployment of MIPS-dependent AI applications.
Accelerated development of more complex and larger-scale AI models due to reduced computational overhead for core operations.
Increased accessibility of advanced AI applications in resource-constrained environments by lowering the computational bar for certain tasks.
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Read at arXiv cs.LG