Search Results for author: Shuchen Song

Found 3 papers, 0 papers with code

Helix: Holistic Optimization for Accelerating Iterative Machine Learning

no code implementations14 Dec 2018 Doris Xin, Stephen Macke, Litian Ma, Jialin Liu, Shuchen Song, Aditya Parameswaran

Machine learning workflow development is a process of trial-and-error: developers iterate on workflows by testing out small modifications until the desired accuracy is achieved.

Helix: Accelerating Human-in-the-loop Machine Learning

no code implementations3 Aug 2018 Doris Xin, Litian Ma, Jialin Liu, Stephen Macke, Shuchen Song, Aditya Parameswaran

Data application developers and data scientists spend an inordinate amount of time iterating on machine learning (ML) workflows -- by modifying the data pre-processing, model training, and post-processing steps -- via trial-and-error to achieve the desired model performance.

Structured Prediction

How Developers Iterate on Machine Learning Workflows -- A Survey of the Applied Machine Learning Literature

no code implementations27 Mar 2018 Doris Xin, Litian Ma, Shuchen Song, Aditya Parameswaran

A quantitative characterization of iteration can serve as a benchmark for machine learning workflow development in practice, and can aid the development of human-in-the-loop machine learning systems.

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