File size: 1,751 Bytes
f712ab1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | ---
task_categories:
- other
---
# When to Align, When to Predict: A Phase Diagram for Multimodal Learning
This repository contains pretrained encoders, cached features, and phase diagnostics data associated with the paper "[When to Align, When to Predict: A Phase Diagram for Multimodal Learning](https://huggingface.co/papers/2606.11190)".
The paper characterizes when contrastive (Cross-Alignment, CA) vs. predictive (Cross-Prediction, CP) self-supervised objectives recover shared signals in multimodal data, particularly for scientific domains.
[Project Website](https://ilaymalinyak.github.io/mm_align_vs_pred/) | [GitHub Repository](https://github.com/IlayMalinyak/mm_align_vs_pred)
## Dataset Description
The data stored here supports experiments including:
- **Astro Data**: Pretrained encoders and cached features for LAMOST × Kepler/TESS spectra.
- **Phase Diagnostics**: Data used to locate real-world datasets in the phase diagram regimes (Both, CA only, CP only, or Neither).
## Sample Usage
You can download the checkpoints, cached features, and phase diagnostics data using the `huggingface_hub` library:
```python
from huggingface_hub import snapshot_download
snapshot_download("Ilayk/mm_align_vs_pred", repo_type="dataset", local_dir="hf_data")
```
After downloading, you can point your environment to the data for the astrophysical experiments:
```bash
export MULTIDESA_ROOT=hf_data
```
## Citation
```bibtex
@article{kamai2026align,
title = {When to Align, When to Predict? A Phase Diagram for Multimodal Self-Supervised Learning},
author = {Kamai, Ilay and Van Assel, Hugues and Regev, Aviv and Perets, Hagai B. and Balestriero, Randall},
journal = {arXiv preprint arXiv:2606.11190},
year = {2026}
}
``` |