Transfer Automatic Machine Learning

By Catherine Wong, Neil Houlsby, Yifeng Lu, Andrea Gesmundo. 2018

10 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. Quanming Yao
2. Qiang Yang
3. Yi-Qi Hu
4. Wei-Wei Tu
5. Mengshuo Wang
6. Hugo Jair Escalante
7. Yang Yu
8. Isabelle M Guyon

Citations in the corpus

• Evolutionary-Neural Hybrid Agents for Architecture Search (2018, methodology): “Similarly to [44], we designed a medium complexity search space of common architectural and hyperparameter choices that define two-tower “wide and deep” models [7].”
• Taking Human out of Learning Applications: A Survey on Automated Machine Learning (2018, methodology): “In [131], a multi-task neural AutoML controller is trained to learn hyperparameters for neural networks.”
• Transferable AutoML by Model Sharing Over Grouped Datasets (2019, methodology): “Unlike multi-task solutions [35, 8, 4] designed for Bayesian optimization, or transfer learning with AutoML [37] based on reinforcement learning, our approach can be easily combined with most existing AutoML techniques in an out-of-box fashion.”