# Deep Active Learning with a Neural Architecture Search

arXiv: https://arxiv.org/abs/1811.07579 · HTML: https://mlsanity.com/paper/1811.07579/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2018.

## Built on by (most cited first, 17 of 17)

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- [FastVLM: Self-Speculative Decoding for Fast Vision-Language Model Inference](https://mlsanity.com/paper/2510.22641/) (2025)
- [Deep Active Learning with Structured Neural Depth Search](https://mlsanity.com/paper/2306.02808/) (2023)
- [ResBuilder: Automated Learning of Depth with Residual Structures](https://mlsanity.com/paper/2308.08504/) (2023)
- [BREATHE: Second-Order Gradients and Heteroscedastic Emulation based Design Space Exploration](https://mlsanity.com/paper/2308.08666/) (2023)
- [ADROIT: A Self-Supervised Framework for Learning Robust Representations for Active Learning](https://mlsanity.com/paper/2503.07506/) (2025)
- [Goal-Oriented Influence-Maximizing Data Acquisition for Learning and Optimization](https://mlsanity.com/paper/2602.19578/) (2026)
- [Position: Stop Preaching and Start Practising Data Frugality for Responsible Development of AI](https://mlsanity.com/paper/2602.19789/) (2026)
- [Are Candidate Models Really Needed for Active Learning?](https://mlsanity.com/paper/2605.14689/) (2026)

## Abstract (arXiv)

We consider active learning of deep neural networks. Most active learning works in this context have focused on studying effective querying mechanisms and assumed that an appropriate network architecture is a priori known for the problem at hand. We challenge this assumption and propose a novel active strategy whereby the learning algorithm searches for effective architectures on the fly, while actively learning. We apply our strategy using three known querying techniques (softmax response, MC-dropout, and coresets) and show that the proposed approach overwhelmingly outperforms active learning using fixed architectures.