Overview
This editorial highlights the upcoming workshop on Program Transformations for Machine Learning, which will take place during the 33rd Conference on Neural Information Processing Systems (NeurIPS) in December 2019. The workshop aims to explore the integration of program transformations within machine learning, a crucial area that can enhance the functionality and efficiency of ML models.
Background & Relevance
Program transformations are essential in machine learning as they allow researchers to manipulate complex models expressed as programs. These transformations, such as automatic differentiation and probabilistic programming, are vital for improving model performance and accessibility. By unifying various approaches to program transformations, this workshop seeks to foster collaboration among researchers from different fields, ultimately accelerating advancements in machine learning methodologies.
Key Details
- Event: Workshop on Program Transformations for Machine Learning
- Date: December 13 or 14, 2019
- Location: Vancouver Convention Centre, Vancouver, BC, Canada
- Website: Program Transformations Workshop
- Submission Deadline: September 16, 2019
- Notification Deadline: October 1, 2019
Eligibility & Participation
The workshop invites contributions from researchers engaged in automatic differentiation, probabilistic programming, programming languages, compilers, and machine learning. It targets those interested in bridging gaps between these domains and enhancing the application of program transformation techniques.
Submission or Application Guidelines
Participants are encouraged to submit extended abstracts ranging from 2 to 4 pages. Submissions should not be anonymized and can include:
– Recent work published in non-ML venues.
– Preliminary or novel work demonstrating applications of program transformation techniques to ML.
– Summaries of previous contributions with potential applications for ML.
Submissions will be evaluated by the organizing committee, with up to six selected for contributed talks based on quality and diversity of research disciplines.
Additional Context / Real-World Relevance
The integration of program transformations in machine learning is becoming increasingly important as ML models grow more complex. By addressing challenges related to hardware and software stacks, this workshop aims to enhance collaboration between the ML and programming languages communities, fostering innovation in model development and application.
Conclusion
The NeurIPS 2019 workshop on Program Transformations for Machine Learning presents a significant opportunity for researchers to share insights and advance the field. Interested participants are encouraged to submit their work and engage with leading experts in the domain, contributing to the evolution of machine learning practices.
Category: CFP & Deadlines
Tags: neurips, machine learning, program transformations, automatic differentiation, probabilistic programming, programming languages, compilers, ml research
