# Meta-Learning Neural Bloom Filters

arXiv: https://arxiv.org/abs/1906.04304 · HTML: https://mlsanity.com/paper/1906.04304/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2019.

## Built on by (most cited first, 10 of 10)

- [Triangle and Four Cycle Counting with Predictions in Graph Streams](https://mlsanity.com/paper/2203.09572/) (2022)
- [A Survey of Learned Indexes for the Multi-dimensional Space](https://mlsanity.com/paper/2403.06456/) (2024)
- [Context Compaction Theory](https://mlsanity.com/paper/2608.01326/) (2026)
- [Learning-based Sketches for Frequency Estimation in Data Streams without Ground Truth](https://mlsanity.com/paper/2412.03611/) (2024)
- [Lego Sketch: A Scalable Memory-augmented Neural Network for Sketching Data Streams](https://mlsanity.com/paper/2505.19561/) (2025)
- [Mergeable Model-Side Aggregation States for Long-Context Language Models](https://mlsanity.com/paper/2607.26448/) (2026)
- [Piecewise Linear Approximation in Learned Index Structures: Theoretical and Empirical Analysis](https://mlsanity.com/paper/2506.20139/) (2025)
- [Learned LSM-trees: Two Approaches Using Learned Bloom Filters](https://mlsanity.com/paper/2508.00882/) (2025)
- [Learned Static Function Data Structures](https://mlsanity.com/paper/2510.27588/) (2025)
- [The Anxiety of Influence: Bloom Filters in Transformer Attention Heads](https://mlsanity.com/paper/2602.17526/) (2026)

## Abstract (arXiv)

There has been a recent trend in training neural networks to replace data structures that have been crafted by hand, with an aim for faster execution, better accuracy, or greater compression. In this setting, a neural data structure is instantiated by training a network over many epochs of its inputs until convergence. In applications where inputs arrive at high throughput, or are ephemeral, training a network from scratch is not practical. This motivates the need for few-shot neural data structures. In this paper we explore the learning of approximate set membership over a set of data in one-shot via meta-learning. We propose a novel memory architecture, the Neural Bloom Filter, which is able to achieve significant compression gains over classical Bloom Filters and existing memory-augmented neural networks.