Analysis of Efficient Neural Architecture Search via Parameter Sharing

By Prabhant Singh. 2019

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Colin White
2. Arber Zela
3. Frank Hutter
4. Stefan Falkner
5. Aaron Klein
6. Xuanyi Dong
7. Yash Savani
8. Sam Nolen

Citations in the corpus

• Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search (2018, background): “Recent work on neural architecture search (NAS) (Baker et al., 2017; Pham et al., 2018; Zoph and Le, 2017; Real et al., 2017; Zoph et al., 2017; Real et al., 2018; Liu et al., 2017; Cai et al., 2018; Elsken et al., 2018) automates this choice of network architecture.”
• Local Search is State of the Art for NAS Benchmarks (2020, methodology): “Similarly, the search spaces from ENAS (Pham et al., 2018) and PNAS (Liu et al., 2018a) contain 10 11 and 10 14 architectures.”