# Using Neural Network Formalism to Solve Multiple-Instance Problems

arXiv: https://arxiv.org/abs/1609.07257 · HTML: https://mlsanity.com/paper/1609.07257/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2016.

## Built on by (most cited first, 5 of 5)

- [Mapping the Internet: Modelling Entity Interactions in Complex Heterogeneous Networks](https://mlsanity.com/paper/2104.09650/) (2021)
- [How to Train your Antivirus: RL-based Hardening through the Problem-Space](https://mlsanity.com/paper/2402.19027/) (2024)
- [Mill.jl and JsonGrinder.jl: automated differentiable feature extraction for learning from raw JSON data](https://mlsanity.com/paper/2105.09107/) (2021)
- [Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data](https://mlsanity.com/paper/2208.02694/) (2022)
- [Malicious Internet Entity Detection Using Local Graph Inference](https://mlsanity.com/paper/2408.03287/) (2024)

## Abstract (arXiv)

Many objects in the real world are difficult to describe by a single numerical vector of a fixed length, whereas describing them by a set of vectors is more natural. Therefore, Multiple instance learning (MIL) techniques have been constantly gaining on importance throughout last years. MIL formalism represents each object (sample) by a set (bag) of feature vectors (instances) of fixed length where knowledge about objects (e.g., class label) is available on bag level but not necessarily on instance level. Many standard tools including supervised classifiers have been already adapted to MIL setting since the problem got formalized in late nineties. In this work we propose a neural network (NN) based formalism that intuitively bridges the gap between MIL problem definition and the vast existing knowledge-base of standard models and classifiers. We show that the proposed NN formalism is effectively optimizable by a modified back-propagation algorithm and can reveal unknown patterns inside bags. Comparison to eight types of classifiers from the prior art on a set of 14 publicly available benchmark datasets confirms the advantages and accuracy of the proposed solution.