Powered by

1st ACM SIGPLAN Workshop on Machine Learning and Programming Languages (MAPL 2017), June 18, 2017, Barcelona, Spain

MAPL 2017 – Proceedings

Contents - Abstracts - Authors

1st ACM SIGPLAN Workshop on Machine Learning and Programming Languages (MAPL 2017)

A Computational Model for TensorFlow: An Introduction
Martín Abadi, Michael Isard, and Derek G. Murray
(Google Brain, USA)

Dyna: Toward a Self-Optimizing Declarative Language for Machine Learning Applications
Tim Vieira, Matthew Francis-Landau, Nathaniel Wesley Filardo, Farzad Khorasani, and Jason Eisner
(Johns Hopkins University, USA; Rice University, USA)

Debugging Probabilistic Programs
Chandrakana Nandi, Dan Grossman, Adrian Sampson, Todd Mytkowicz, and Kathryn S. McKinley
(University of Washington, USA; Cornell University, USA; Microsoft Research, USA; Google, USA)

Combining the Logical and the Probabilistic in Program Analysis
Xin Zhang, Xujie Si, and Mayur Naik
(Georgia Institute of Technology, USA; University of Pennsylvania, USA)

Learning a Classifier for False Positive Error Reports Emitted by Static Code Analysis Tools
Ugur Koc, Parsa Saadatpanah, Jeffrey S. Foster, and Adam A. Porter
(University of Maryland at College Park, USA)

Verified Perceptron Convergence Theorem
Charlie Murphy, Patrick Gray, and Gordon Stewart
(Princeton University, USA; Ohio University, USA)

proc time: 0.63