Relationship between receptive field and learnable bandwidth
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 10
- Forks
- 6
- PR merge metrics
- No merged PRs in 30d
Description
#30 suggests a potentially interesting relationship between DeepClean's effective receptive field and the bandwidth of signals it can model. If this is true, it would be cool to try to derive what this tradeoff ought to be analytically from the uncertainty principle and then validate it on either synthetic or real data (or both).
Contributor guide
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
Start by reviewing issue #30 and the proposed relationship between DeepClean's effective receptive field and learnable signal bandwidth. Derive the expected tradeoff analytically from the uncertainty principle, then validate it using synthetic data, real data, or both. Done means the relationship is supported or challenged by the analysis and validation results.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100