license: cc0-1.0
tags:
- research-notes
- data-efficient-learning
Research notes: Data Efficient Learning
Overview
This repository contains a working research note about Data Efficient Learning. It organizes motivation, related work, a falsifiable hypothesis, and an evaluation plan. It is not presented as a completed paper or a release of trained models.
Acknowledgments
This repo borrows ideas freely from public reference implementations; credit to their authors.
Open questions
- which baselines are actually comparable under matched capacity?
- what is the minimum data exposure for a meaningful signal?
- which confounders would invalidate a naive comparison?
Scope and limitations
Exploratory by design: no benchmark claims, no completed ablations, no released code, no trained checkpoint. The references are a starting point for verification, not evidence that the study has run.
Contact
Questions or corrections: open a discussion in this repository.
How to read this repository
Read paper_notes.md first. Anything labeled as a plan or hypothesis is not a result. Future results should ship with dataset versions, commands, seeds, hardware and raw logs.
What is covered
- research question scope and likely confounders
- one proposed comparison against matched baselines
- evaluation context such as task-appropriate public benchmarks named in the main note
- reproducibility checks and open questions
Files
| File | Purpose |
|---|---|
paper_notes.md |
primary artifact |
README.md |
this documentation |
config.json |
architecture configuration |
training_args.json |
default experiment settings |
model.safetensors |
initialization checkpoint |
NOTES.md |
informal to-do notes |
License
Distributed under the cc0-1.0 license. External datasets keep their own terms — check them separately.