提出可动态组合的模块化神经草图，提升数据流压缩的适应性与精度。
Lego Sketch: A Scalable Memory-augmented Neural Network for Sketching Data Streams
- 用模块化记忆块动态构建神经草图，适配不同存储预算和数据类型。
- 理论证明具有高可扩展性，并首次给出神经草图的误差上界。
- 实测优于现有手工及神经草图，在空间-精度权衡上表现更优。
草图是用于在有限空间内估计无限数据流中项目频率的概率结构，在多个领域广泛应用。近期研究从手工草图转向神经草图，利用记忆增强神经网络（MANN）提升流式压缩能力并实现更好的空间-精度权衡。然而，现有神经草图因MANN配置僵化，难以在不同数据域和空间预算下扩展。本文提出一种可扩展的MANN架构，实现名为“Lego sketch”的新型草图。如同用乐高积木拼搭，Lego sketch通过动态协调多个记忆块，适应多样空间预算与数据域。理论分析证明其高可扩展性，并首次为神经草图提供误差上界。大量实验表明，Lego sketch在空间-精度权衡上显著优于现有手工与神经草图。代码已开源：https://github.com/FFY0/LegoSketch_ICML。
原文摘要 · Abstract (English)
Sketches, probabilistic structures for estimating item frequencies in infinite data streams with limited space, are widely used across various domains. Recent studies have shifted the focus from handcrafted sketches to neural sketches, leveraging memory-augmented neural networks (MANNs) to enhance the streaming compression capabilities and achieve better space-accuracy trade-offs.However, existing neural sketches struggle to scale across different data domains and space budgets due to inflexible MANN configurations. In this paper, we introduce a scalable MANN architecture that brings to life the {\it Lego sketch}, a novel sketch with superior scalability and accuracy. Much like assembling creations with modular Lego bricks, the Lego sketch dynamically coordinates multiple memory bricks to adapt to various space budgets and diverse data domains. Our theoretical analysis guarantees its high scalability and provides the first error bound for neural sketch. Furthermore, extensive experimental evaluations demonstrate that the Lego sketch exhibits superior space-accuracy trade-offs, outperforming existing handcrafted and neural sketches. Our code is available at https://github.com/FFY0/LegoSketch_ICML.
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