mlsanityData to 14 Oct 2026

Deep Active Learning with a Neural Architecture Search

arXiv 1811.07579 ↗First posted 19 Nov 2018cs.LG stat.MLcited by 17

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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.

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

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Citing papers per year: 2022: 1; 2023: 9; 2024: 1; 2025: 3; 2026: 3.

17 papers.

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  1. 2024A Survey on Deep Active Learning: Recent Advances and New Frontierscited by 54
  2. 2023Active Prompt Learning in Vision Language Modelscited by 25
  3. 2023Label-Efficient Learning in Agriculture: A Comprehensive Reviewcited by 16
  4. 2023Self-Expanding Neural Networkscited by 14
  5. 2023Efficient Automation of Neural Network Design: A Survey on Differentiable Neural Architecture Searchcited by 5
  6. 2023DP-HyPO: An Adaptive Private Hyperparameter Optimization Frameworkcited by 4
  7. 2022Integrating Transformer and Autoencoder Techniques with Spectral Graph Algorithms for the Prediction of Scarcely Labeled Molecular Datacited by 2
  8. 2023Active Learning for Video Classification with Frame Level Queriescited by 2
  9. 2025Causal Discovery via Bayesian Optimizationcited by 1
  10. 2025FastVLM: Self-Speculative Decoding for Fast Vision-Language Model Inferencecited by 1
  11. 2023Deep Active Learning with Structured Neural Depth Searchcited by 0
  12. 2023ResBuilder: Automated Learning of Depth with Residual Structurescited by 0
5 more
  1. 2023BREATHE: Second-Order Gradients and Heteroscedastic Emulation based Design Space Explorationcited by 0
  2. 2025ADROIT: A Self-Supervised Framework for Learning Robust Representations for Active Learningcited by 0
  3. 2026Goal-Oriented Influence-Maximizing Data Acquisition for Learning and Optimizationcited by 0
  4. 2026Position: Stop Preaching and Start Practising Data Frugality for Responsible Development of AIcited by 0
  5. 2026Are Candidate Models Really Needed for Active Learning?cited by 0