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‎sigir-2025.html‎

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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8">
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<meta name="description"
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content="Deformable Neural Radiance Fields creates free-viewpoint portraits (nerfies) from casually captured videos.">
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<meta name="keywords" content="Nerfies, D-NeRF, NeRF">
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<title>Efficient In-Memory Inverted Indexes: Theory and Practice</title>
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More Research
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<a class="navbar-item" href="https://hypernerf.github.io">
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HyperNeRF
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Nerfies
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-->
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<div class="column has-text-centered">
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<h1 class="title is-1 publication-title">Efficient In-Memory Inverted Indexes: Theory and
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Practice</h1>
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<div class="is-size-5 publication-authors">
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<span class="author-block">
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<a href="https://jmmackenzie.io/">Joel Mackenzie</a><sup>1</sup>,</span>
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<span class="author-block">
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<a href="https://macavaney.us/">Sean MacAvaney</a><sup>2</sup>,</span>
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<span class="author-block">
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<a href="https://antoniomallia.it">Antonio Mallia</a><sup>3</sup>,
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</span>
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<span class="author-block">
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<a href="https://siedlaczek.me/">Michal Siedlaczek</a><sup>4</sup>,
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</span>
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</div>
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<div class="is-size-5 publication-authors">
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<span class="author-block"><sup>1</sup>The University of Queensland,</span>
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<span class="author-block"><sup>2</sup>University of Glasgow,</span>
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<span class="author-block"><sup>3</sup>Pinecone,</span>
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<span class="author-block"><sup>4</sup>MongoDB, Inc.</span>
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<a href="https://arxiv.org/pdf/2011.12948"
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<span>Paper</span>
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<a href="https://arxiv.org/abs/2011.12948"
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class="external-link button is-normal is-rounded is-dark">
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<i class="ai ai-arxiv"></i>
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<span>arXiv</span>
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<a href="https://github.com/google/nerfies"
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<span>Code</span>
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<a href="https://github.com/google/nerfies/releases/tag/0.1"
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class="external-link button is-normal is-rounded is-dark">
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<span>Data</span>
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<!-- Abstract. -->
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<div class="column is-four-fifths">
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<h2 class="title is-3">Abstract</h2>
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<div class="content has-text-justified">
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<p>
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Inverted indexes are the backbone of most large-scale information retrieval systems.
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Although conceptually simple, high-performance inverted indexes require a deep understanding
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of low-level system optimizations, compression techniques, and traversal strategies. With
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the widespread adoption of in-memory search engines, the rise of learned sparse retrieval
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(LSR), and the increasing complexity of ranking pipelines, the design space for efficient
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indexing and retrieval systems has expanded significantly.
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</p>
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<p>
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This tutorial addresses a critical knowledge gap between textbook-style explanations and
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advanced techniques required for efficient and optimized retrieval. It aims to equip
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researchers and practitioners with a comprehensive understanding of how modern in-memory
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search systems are designed, built, and optimized for high-performance retrieval across
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large-scale document collections.
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</p>
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This website is licensed under a <a rel="license"
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