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README.md

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+---
+license: cc-by-4.0
+tags:
+- research-notes
+- data-efficient-learning
+---
+
+# Notes on Data Efficient Learning
+
+## Repository summary
+
+This is an exploratory note for **Data Efficient Learning**. It records the intended comparison, likely confounders, and reproducibility requirements before any benchmark result is reported.
+
+## What is covered
+
+- the scope of the research question and likely confounders
+- a proposed comparison with matched baselines
+- concrete evaluation context such as task-appropriate public benchmarks named in the main note
+- reproducibility checks, failure modes, and open questions
+- topic-relevant references
+
+## How to read this repository
+
+Start with `analysis.md` for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds, hardware, and raw logs.
+
+## Scope and limitations
+
+The note is intentionally exploratory. It does not claim benchmark improvements, completed ablations, released code, or a trained checkpoint. References and proposed datasets provide a starting point for verification rather than evidence that the study has already been run.
+
+## Files
+
+- `analysis.md` — primary artifact
+- `README.md` — this documentation
+
+## License
+
+Released under **cc-by-4.0**. Review the source-data terms separately when this repository is used with external datasets.

analysis.md

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+# Data Efficient Learning: Research Notes
+
+## Status
+
+Working note / experiment plan. No completed benchmark results are claimed here.
+
+## 1. Scope and motivation
+
+This document is a research sketch about representation quality under limited labels, transfer, or compressed supervision. The central question is whether the proposed change improves the target behavior under a matched training and evaluation budget. The note deliberately separates hypotheses from observations so that future results can be added without rewriting the rationale.
+
+## 2. Context
+
+Research on data efficient learning often mixes improvements from architecture, data scale, preprocessing, and compute. A useful comparison therefore needs controlled baselines and explicit reporting of resource use. For this topic, the main confound is that comparisons are sensitive to pretraining data, augmentations, and tuning budgets.
+
+## 3. Working hypothesis
+
+A focused change to the representation or interaction mechanism may improve linear-probe accuracy without increasing deployment cost disproportionately. The hypothesis should be rejected if gains disappear after matching parameter count, data exposure, or tuning budget.
+
+## 4. Proposed approach
+
+The first implementation should keep modality-specific preprocessing simple, project inputs into a shared representation space, and isolate the new component behind a small interface. Baselines should include a comparable model without the component and a stronger off-the-shelf reference. Any optimization should be applied to all systems, not only the proposed one.
+
+## 5. Evaluation plan
+
+| Dataset | Role | Primary measure |
+|---|---|---|
+| ImageNet-1K | primary evaluation | linear-probe accuracy |
+| Flickr30k | transfer / robustness | Recall@1 |
+| VQAv2 | transfer / robustness | sample efficiency |
+
+Planned comparisons include a matched-capacity baseline, an ablation that removes the proposed component, and an out-of-domain transfer check. Default training values for the first controlled run are learning rate `5e-05`, batch size `16`, and `3` independent seeds. These are planning values, not claims about a finished experiment.
+
+## 6. Reproducibility checklist
+
+- Publish exact split identifiers.
+- Log package versions and hardware.
+- Tune baselines under the same budget.
+- Save both aggregate and per-category metrics.
+
+## 7. Failure modes and responsible use
+
+The analysis should report subgroup and category-level failures instead of relying only on a single aggregate score. Particular attention is needed because comparisons are sensitive to pretraining data, augmentations, and tuning budgets. No production use is recommended without task-specific validation, data review, and an assessment of privacy and bias.
+
+## 8. Open questions
+
+- How sensitive is the conclusion to preprocessing and random seed?
+- Does the proposed component improve calibration as well as the primary metric?
+- Where does the method fail on compositional or out-of-domain examples?
+
+## References
+
+[1] Chen et al., SimCLR, 2020.
+[2] He et al., MAE, 2022.
+[3] Hinton et al., Knowledge Distillation, 2015.

config.json

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+{
+  "architectures": [
+    "CustomResearchModel"
+  ],
+  "architecture": "transformer",
+  "model_type": "transformer",
+  "hidden_size": 192,
+  "num_hidden_layers": 3,
+  "num_attention_heads": 2,
+  "intermediate_size": 384,
+  "hidden_act": "gelu",
+  "max_position_embeddings": 512,
+  "layer_norm_eps": 1e-12,
+  "checkpoint_status": "initialization-only",
+  "notes": "Untrained checkpoint for smoke tests; no benchmark claim."
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0e2b0c4a390fdd9c7046947a2d04638201de8caa2429542dbe4344e9322876cc
+size 99808

training_args.json

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+{
+  "optimizer": "adamw",
+  "scheduler": "cosine",
+  "learning_rate": 5e-05,
+  "batch_size": 24,
+  "epochs": 30,
+  "seed": 3407,
+  "status": "default recipe; not a completed run"
+}