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
Time Schedule
The general time schedule can be found here: /computational/#time-schedule
- Meetings
- On zoom: [Zoom link removed] as needed
- The date and time of future meetings will be announced via email and slack
- Mailing List instructions Our list: snowmass-compf3-ml@listserv.fnal.gov
- Slack instructions (very end of page)
- Nov. 2020 - Dec. 2020
Submitted LOI
Here is the list of submitted LOIs to this topical group. First index before “/” corresponds to the primary frontier used for the submission.