Neural Architecture Search with GBDT

By Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Enhong Chen, Tie-Yan Liu. 2020

32 citations recorded by the source corpus.

How researchers see this paper

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

1. Xu Tan
2. Tao Qin
3. Tie-Yan Liu
4. Renqian Luo
5. Julien N. Siems
6. Arber Zela
7. Sheng Zhao
8. Frank K. Soong

Citations in the corpus

• A Survey on Neural Speech Synthesis (2021, background): “LightSpeech [214] leverages neural architecture search [450, 213] to find lightweight architectures to further speed up the inference of FastSpeech 2 [286] by 6.”
• NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search (2020, methodology): “…gradient boosting methods XGBoost (Chen & Guestrin, 2016) and LGBoost (Ke et al., 2017), which have recently been used for predictor-based NAS (Luo et al., 2020 2017) we assess the quality of the data fit via the coefficient of determination ( R 2 ) and the Kendall rank correlation coeffi-cient…”
• Lightspeech: Lightweight and Fast Text to Speech with Neural Architecture Search (2021, methodology): “Thirdly, among many well-performing NAS algorithms, we adopt accuracy prediction based NAS [17, 18] which is straightforward and efficient.”