computational:machine_learning
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Table of Contents
CompF3: Machine Learning
Working Group Co-Conveners
| Name | Institution | |
|---|---|---|
| Phiala Shanahan | MIT | pshana[at]mit.edu |
| Kazuhiro Terao | SLAC | kterao[at]slac.stanford.edu |
| Daniel Whiteson | Irvine | daniel[at]uci.edu |
Description
- Functional areas
- Machine learning (both training and inference) at scale both for big models as well as massively parallel training and inference of many models
- Supporting both individual researchers training and deploying their own models, as well as centralized production workflows using machine learning at scale
- Fast inference and training
- Calibration/validation
- Mandate
- Describe the machine learning training and inference needs of the stakeholders
- What are the resources needed to execute these workflows?
- What is the technology evolution of these resources?
- Coordinate with experimental algorithms and theoretical calculations and simulations
- How will the stakeholders be able to design and write machine learning applications for these resources
- Are there standards that the community should follow?
- How are the solutions used by the community embedded/derived from solutions from industry/other science domains
computational/machine_learning.1595371932.txt.gz · Last modified: 2020/07/21 17:52 by sg_indiana.edu