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Wed 11 Nov 2020 17:39 - 17:40 at Virtual room 2 - ML Model Building

Success using machine learning (ML) in numerous fields has created a new class of users, who are not experts in the data science domain but want to use ML as a means to solve their inference problems. Various automatic machine learning (AutoML) approaches attempt to make ML solutions accessible to such users. In this work, we present a system that automatically synthesizes correct code within the context of the user’s data using sketching. In sketching, insight is determined through a partial program; a sketch expresses the high-level structure of implementation but leaves holes in place of the low-level details. We use meta-learning on meta-features to approximately solve holes. We observe that the sketch-based approach is more expressive, easier to implement, and easier to optimize than existing AutoML frameworks. Our initial results are very promising. Our approach uses fewer resources and still produces comparable results to existing techniques.

Wed 11 Nov

Displayed time zone: (UTC) Coordinated Universal Time change

17:30 - 18:00
17:30
2m
Talk
AMS: Generating AutoML Search Spaces from Weak Specifications
Research Papers
José Pablo Cambronero Massachusetts Institute of Technology, USA, Jürgen Cito TU Wien and MIT, Martin C. Rinard Massachusetts Institute of Technology, USA
DOI
17:33
1m
Talk
Continuous Experimentation on Artificial Intelligence Software: A Research Agenda
Visions and Reflections
Anh Nguyen-Duc University of South Eastern Norway, Pekka Abrahamsson University of Jyväskylä
DOI
17:35
1m
Talk
DENAS: Automated Rule Generation by Knowledge Extraction from Neural Networks
Research Papers
Simin Chen University of Texas at Dallas, USA, Soroush Bateni University of Texas at Dallas, USA, Sampath Grandhi University of Texas at Dallas, USA, Xiaodi Li University of Texas at Dallas, USA, Cong Liu University of Texas at Dallas, USA, Wei Yang University of Texas at Dallas, USA
DOI
17:37
1m
Talk
On Decomposing a Deep Neural Network into ModulesACM SIGSOFT Distinguished Paper Award
Research Papers
Rangeet Pan Iowa State University, USA, Hridesh Rajan Iowa State University, USA
DOI Media Attached
17:39
1m
Talk
Synthesizing Correct Code for Machine Learning Programs
Student Research Competition
Joshua Gisi North Dakota State University, USA
DOI
17:41
19m
Talk
Conversations on ML Model Building
Paper Presentations
José Pablo Cambronero Massachusetts Institute of Technology, USA, Rangeet Pan Iowa State University, USA, Simin Chen , Wei Yang University of Texas at Dallas, USA, M: John-Paul Ore North Carolina State University