The Role of Massively Multi-Task and Weak Supervision in Software 2.0

By Alexander J. Ratner, Braden Hancock, Christopher R'e. 2019

29 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. Steven Euijong Whang
2. Yuji Roh
3. Geon Heo
4. Vincent S. Chen
5. Zhenzhen Weng
6. Peter Christen
7. Xin He

Citations in the corpus

• A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, background): “As a result, weak supervision techniques [151], [152], [153] have become increasingly popular where the idea is to”
• Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices (2019, methodology): “We validate the efficacy of quality and noise estimation in SRAMs and compare to weak supervision frameworks [26] that estimate the quality of supervision sources to improve overall model accuracy.”