SysML is a conference targeting research at the intersection of systems and machine learning. The conference aims to elicit new connections amongst these fields, including identifying best practices and design principles for learning systems, as well as developing novel learning methods and theory tailored to practical machine learning workflows.
The SysML conference will be held on March 31st, April 1st, and April 2nd, 2019 in Stanford, California. Submission information can be found below; please check the website in the following weeks for additional details on the conference schedule and logistics.
Authors are encouraged to submit previously unpublished research at the intersection of computer systems and machine learning. The SysML Program Committee will select papers based on a combination of novelty, quality, interest, and impact.
Topics of interest include, but are not limited to:
- Efficient model training, inference, and serving
- Distributed and parallel learning algorithms
- Privacy and security for ML applications
- Testing, debugging, and monitoring of ML applications
- Fairness and interpretability for ML applications
- Data preparation, feature selection, and feature extraction
- ML programming models and abstractions
- Programming languages for machine learning
- Visualization of data, models, and predictions
- Customized hardware for machine learning
- Hardware-efficient ML methods
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Stanford , United States