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Monday, May 20 • 3:10pm - 3:30pm
Manifold: A Model-Agnostic Visual Debugging Tool for Machine Learning at Uber

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Interpretation and diagnosis of machine learning models have gained renewed interest in recent years with breakthroughs in new approaches. We present Manifold, Uber’s in-house model-agnostic visualization tool for ML performance diagnosis and model debugging. Manifold utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models in a more transparent and interactive manner. We demonstrate current applications of the Manifold on the classification and regression tasks at Uber and discuss other potential machine learning use scenarios where Manifold can be applied.


Lezhi Li

Uber Inc.

Yunfeng Bai

Uber Inc.

Yang Wang

Uber Inc.
Yang Wang is a Sr. Research Engineer leading the Machine Learning Visualization team at Uber. His research interests lie in Human-Computer Interaction and High-Performance Computing, specifically, methodologies and systems to model the Interpretability and Actionability of AI-aided... Read More →

Monday May 20, 2019 3:10pm - 3:30pm PDT
Stevens Creek Room