# Auxiliary Network: Scalable and agile online learning for dynamic system with inconsistently available inputs

arXiv: https://arxiv.org/abs/2008.11828 · HTML: https://mlsanity.com/paper/2008.11828/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2020.

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

- [Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts](https://mlsanity.com/paper/2303.05155/) (2023)
- [Online Learning under Haphazard Input Conditions: A Comprehensive Review and Analysis](https://mlsanity.com/paper/2404.04903/) (2024)
- [Hedging Is Not All You Need: A Simple Baseline for Online Learning Under Haphazard Inputs](https://mlsanity.com/paper/2409.10242/) (2024)
- [packetLSTM: Dynamic LSTM Framework for Streaming Data with Varying Feature Space](https://mlsanity.com/paper/2410.17394/) (2024)
- [Haphazard Inputs as Images in Online Learning](https://mlsanity.com/paper/2504.02912/) (2025)

## Abstract (arXiv)

Streaming classification methods assume the number of input features is fixed and always received. But in many real-world scenarios demand is some input features are reliable while others are unreliable or inconsistent. In this paper, we propose a novel deep learning-based model called Auxiliary Network (Aux-Net), which is scalable and agile. It employs a weighted ensemble of classifiers to give a final outcome. The Aux-Net model is based on the hedging algorithm and online gradient descent. It employs a model of varying depth in an online setting using single pass learning. Aux-Net is a foundational work towards scalable neural network model for a dynamic complex environment requiring ad hoc or inconsistent input data. The efficacy of Aux-Net is shown on public dataset.