Please visit https://sites.google.com/view/mapl2017/home for detailed information on the MAPL workshop.
Due to recent algorithmic and computational advances, machine learning has seen a surge of interest in both research and practice. From natural language processing to self-driving cars, machine learning is creating new possibilities that are changing the way we live and interact with computers. However, the impact of these advances on programming languages remains mostly untapped. Yet, incredible research opportunities exist when combining machine learning and programming languages in novel ways. MAPL seeks to bring together programming language and machine learning communities to encourage collaboration and exploration in cross disciplinary research. The workshop will include a combination of peer-reviewed papers and invited events, such as invited talks, panels and/or town hall discussions.
MAPL seeks papers on a diverse range of topics related to programming languages and machine learning including:
- Programming languages and compilers for machine learning
- Deep learning frameworks
- Machine learning for compilation and run-time scheduling
- Improving programmer productivity via machine learning
- Inductive programming
- Formal verification of machine learning systems
- Probabilistic programming
- Collaborative human / computer programming
- Interoperability of machine learning frameworks and existing code bases
Call for Papers
MAPL paper submissions should be made through EasyChair (link to follow soon).
Papers must be submitted in PDF and be no more than 8 pages in standard two-column SIGPLAN conference format including figures and tables but not including references. Shorter submissions are welcome. The submissions will be judged based on the merit of the ideas rather than the length. Submissions must be made through the on-line submission site. Formal proceedings will be included in the ACM digital archive and available at the workshop.
Sun 18 Jun Times are displayed in time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
09:15 - 09:30
|Introduction and Welcome|
09:30 - 10:30
|Programming by Examples: PL Meets ML|
Sumit GulwaniMicrosoft Research
11:00 - 12:00
|A Computational Model for TensorFlow (An Introduction)|
|Dyna: Toward a Self-Optimizing Declarative Language for Machine Learning Applications|
12:00 - 12:30
|Debugging Probabilistic Programs|
14:00 - 15:30
|Combining the Logical and the Probabilistic in Program Analysis|
|Learning a Classifier for False Positive Error Reports Emitted by Static Code Analysis Tools|
|Verified Perceptron Convergence Theorem|