RosettaCommons / RosettaCommons/RFdiffusion
How to describe the loss at different timesteps?
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 3.1k
- Forks
- 644
- PR merge metrics
- No merged PRs in 30d
Description
@joewatchwell @lyskov
This is really excellent work. I have some questions regarding this work.
When training the model, how do you appropriately describe the loss for protein trajectories? Is the loss treated equally for each timestep t? When t approaches T, the structural loss should be relatively large, while it should be relatively small when t approaches 1.
In fact, when t=T or t=1, real protein structures are used for supervision. This seems unreasonable or difficult, as it was impossible to obtain the same protein structure as the label when t=T.
Looking forward to your reply!
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No file, test, or entry point is named. Start by locating the training loss implementation and timestep handling, then determine whether the issue can be answered from the existing behavior; done would require a documented explanation of loss weighting and supervision at t=1 and t=T.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- bioinformatics, machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 15/100