Skip to content
This repository has been archived by the owner on Jan 24, 2023. It is now read-only.

Improve optimistic selection #136

Open
ch3njust1n opened this issue Sep 11, 2018 · 0 comments
Open

Improve optimistic selection #136

ch3njust1n opened this issue Sep 11, 2018 · 0 comments
Assignees
Labels
enhancement New feature or request

Comments

@ch3njust1n
Copy link
Owner

threshold for forming a hyperdge. if all peers in party are already at good points in space, then no need to form an edge and train, which would result in wasting all the progress for the U-1 (uniformity minus one) number of peers in the hyperedge. e.g. (A, 97.2), (B, 97.25), (C, 97.4), but A, B, C are all at different points in hyperparameter and parameter space. Then there's no need to form a hyperedge. Weaker workers - possibly stuck in a local minima should train with stronger models to better utilize computation. Need a threshold for how far apart in performance peers must be in order to form a hyperedge.

@ch3njust1n ch3njust1n added the enhancement New feature or request label Sep 11, 2018
@ch3njust1n ch3njust1n self-assigned this Sep 11, 2018
Sign up for free to subscribe to this conversation on GitHub. Already have an account? Sign in.
Labels
enhancement New feature or request
Projects
None yet
Development

No branches or pull requests

1 participant