262 citations · 628 across the 10 of their papers we have counts for
11 papers
Neural Architecture Search: Insights from 1000 Papers
Colin White, Mahmoud Safari, Rhea Sukthanker +5
In the past decade, advances in deep learning have resulted in breakthroughs in a variety of areas, including computer vision, natural language understanding, speech recognition, a…
Neural Architecture Search for Dense Prediction Tasks in Computer Vision
Thomas Elsken, Arber Zela, Jan Hendrik Metzen +4
The success of deep learning in recent years has lead to a rising demand for neural network architecture engineering. As a consequence, neural architecture search (NAS), which aims…
Bag of Tricks for Neural Architecture Search
Thomas Elsken, Benedikt Staffler, Arber Zela +2
While neural architecture search methods have been successful in previous years and led to new state-of-the-art performance on various problems, they have also been criticized for…
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization
Julia Guerrero-Viu, Sven Hauns, Sergio Izquierdo +7
Neural architecture search (NAS) and hyperparameter optimization (HPO) make deep learning accessible to non-experts by automatically finding the architecture of the deep neural net…
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift
Sheheryar Zaidi, Arber Zela, Thomas Elsken +3
Ensembles of neural networks achieve superior performance compared to stand-alone networks in terms of accuracy, uncertainty calibration and robustness to dataset shift. \emph{Deep…
Meta-Learning of Neural Architectures for Few-Shot Learning
Thomas Elsken, Benedikt Staffler, Jan Hendrik Metzen +1
The recent progress in neural architecture search (NAS) has allowed scaling the automated design of neural architectures to real-world domains, such as object detection and semanti…