FeatureHub: Towards Collaborative Data Science

By Micah J. Smith, Roy Wedge, Kalyan Veeramachaneni. 2017

20 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Information Systems

How researchers see this paper

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

In Artificial Intelligence

1. Marco F. Huber
2. Marc-André Zöller
3. Wei-Wei Tu
4. Quanming Yao
5. Yang Yu
6. Qiang Yang
7. Yi-Qi Hu
8. Isabelle M Guyon

In Computer Science Applications

1. Marc-André Zöller
2. Qiang Yang
3. Marco F. Huber

In Information Systems

1. Marc-André Zöller
2. Marco F. Huber
3. Yang Yu
4. Qiang Yang
5. Hugo Jair Escalante
6. Isabelle M Guyon
7. Yu-Feng Li
8. Quanming Yao

Citations in the corpus

• Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “, (Friedman and Markovitch, 2015; Smith et al., 2017), are not considered as AutoML aims to be domain-agnostic.”
• Survey on Automated Machine Learning (2019, background): “Approaches to enhance automatic feature generation with domain knowledge, e.g., (Friedman and Markovitch, 2015; Smith et al., 2017), are therefore not considered.”
• Taking Human out of Learning Applications: A Survey on Automated Machine Learning (2018, background): “Existing works on this topic include Data Science Machine (DSM) [10], ExploreKit [9] and FeatureHub [22].”