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license: mit
library_name: pytorch
tags:
- hdtree
- pytorch
- mnist
- single-cell
- clustering
---
# HDTree ICML Checkpoints
This repository hosts pretrained checkpoints for the public HDTree ICML release.
Paper:
- ICML 2026 poster page: https://icml.cc/virtual/2026/poster/61839
- arXiv abstract: https://arxiv.org/abs/2506.23287
- arXiv PDF: https://arxiv.org/pdf/2506.23287
Code: https://github.com/zangzelin/code_HDTree_icml
## Files
| File | Dataset | Configuration | Notes |
|---|---|---|---|
| `checkpoints/mnist/hdtree_mnist_best_epoch59_acc0.97570.pth` | MNIST | `configs/mnist.yaml` | Best MNIST checkpoint from the full run by checkpoint validation accuracy. |
| `checkpoints/limb/hdtree_limb_i10_epoch199_acc0.53921.pth` | Limb | `configs/limb.yaml` default | Limb sweep i10/default checkpoint. |
## Reported Metrics
MNIST full run summary:
| ACC | DP | LP | NMI |
|---:|---:|---:|---:|
| 0.97310 | 0.93262 | 0.97310 | 0.92999 |
Limb i10 run summary (`batch_size=1000`, `K=10`, `exaggeration_lat=0.5`, `nu_lat=0.3`):
| ACC | DP | LP | NMI |
|---:|---:|---:|---:|
| 0.52860 | 0.41029 | 0.58370 | 0.49042 |
The included `logs/` files contain the original run outputs used to record these metrics.
## Download
```bash
pip install huggingface_hub
huggingface-cli download zangzelin/HDTree-ICML-checkpoints --local-dir .
```
Then use the public code repository's validation script, for example:
```bash
bash scripts/validate_checkpoint.sh checkpoints/limb/hdtree_limb_i10_epoch199_acc0.53921.pth
```
## Checksums
See `SHA256SUMS`.
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