# NADS: Neural Architecture Distribution Search for Uncertainty Awareness

arXiv: https://arxiv.org/abs/2006.06646 · HTML: https://mlsanity.com/paper/2006.06646/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

## Built on by (most cited first, 8 of 8)

- [Neural Architecture Search: Insights from 1000 Papers](https://mlsanity.com/paper/2301.08727/) (2023)
- [NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization](https://mlsanity.com/paper/2109.02038/) (2021)
- [NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research](https://mlsanity.com/paper/2211.11747/) (2022)
- [Approximate Neural Architecture Search via Operation Distribution Learning](https://mlsanity.com/paper/2111.04670/) (2021)
- [Unsupervised Domain Adaptation Architecture Search with Self-Training for Land Cover Mapping](https://mlsanity.com/paper/2404.14704/) (2024)
- [μDARTS: Model Uncertainty-Aware Differentiable Architecture Search](https://mlsanity.com/paper/2107.11500/) (2021)
- [MRF-UNets: Searching UNet with Markov Random Fields](https://mlsanity.com/paper/2207.06168/) (2022)
- [VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition](https://mlsanity.com/paper/2204.00130/) (2022)

## Abstract (arXiv)

Machine learning (ML) systems often encounter Out-of-Distribution (OoD) errors when dealing with testing data coming from a distribution different from training data. It becomes important for ML systems in critical applications to accurately quantify its predictive uncertainty and screen out these anomalous inputs. However, existing OoD detection approaches are prone to errors and even sometimes assign higher likelihoods to OoD samples. Unlike standard learning tasks, there is currently no well established guiding principle for designing OoD detection architectures that can accurately quantify uncertainty. To address these problems, we first seek to identify guiding principles for designing uncertainty-aware architectures, by proposing Neural Architecture Distribution Search (NADS). NADS searches for a distribution of architectures that perform well on a given task, allowing us to identify common building blocks among all uncertainty-aware architectures. With this formulation, we are able to optimize a stochastic OoD detection objective and construct an ensemble of models to perform OoD detection. We perform multiple OoD detection experiments and observe that our NADS performs favorably, with up to 57% improvement in accuracy compared to state-of-the-art methods among 15 different testing configurations.