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Wed 11 Nov 2020 17:37 - 17:38 at Virtual room 2 - ML Model Building

Deep learning is being incorporated in many modern software systems. Deep learning approaches train a deep neural network (DNN) model using training examples, and then use the DNN model for prediction. While the structure of a DNN model as layers is observable, the model is treated in its entirety as a monolithic component. To change the logic implemented by the model, e.g. to add/remove logic that recognizes inputs belonging to a certain class, or to replace the logic with an alternative, the training examples need to be changed and the DNN needs to be retrained using the new set of examples. We argue that decomposing a DNN into DNN modules— akin to decomposing a monolithic software code into modules—can bring the benefits of modularity to deep learning. In this work, we develop a methodology for decomposing DNNs for multi-class problems into DNN modules. For four canonical problems, namely MNIST, EMNIST, FMNIST, and KMNIST, we demonstrate that such decomposition enables reuse of DNN modules to create different DNNs, enables replacement of one DNN module in a DNN with another without needing to retrain. The DNN models formed by composing DNN modules are at least as good as traditional monolithic DNNs in terms of test accuracy for our problems.

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
Jose 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
SiminChen 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
Jose 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