
This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from pythae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_svae")
```

## Reproducibility
This trained model reproduces the results of Table 1 in [1].

| Model | Dataset | Metric | Obtained value | Reference value |
|:---:|:---:|:---:|:---:|:---:|
| SVAE | Dyn. Binarized MNIST | NLL (500 IS) | 93.13 (0.01) | 93.16 (0.31) |

[1] Tim R Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, and Jakub M Tomczak. Hyperspherical variational auto-encoders. In 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018, pages 856–865. Association For Uncertainty in Artificial Intelligence (AUAI), 2018.
{"language": "en", "license": "apache-2.0", "tags": ["pythae", "reproducibility"]}
pythae reproducibility en license:apache-2.0 region:us
TAGS
#pythae #reproducibility #en #license-apache-2.0 #region-us 

This model was trained with pythae. It can be downloaded or reloaded using the method 'load\_from\_hf\_hub'


Reproducibility
---------------


This trained model reproduces the results of Table 1 in [1].



[1] Tim R Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, and Jakub M Tomczak. Hyperspherical variational auto-encoders. In 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018, pages 856–865. Association For Uncertainty in Artificial Intelligence (AUAI), 2018.



TAGS
#pythae #reproducibility #en #license-apache-2.0 #region-us 

TAGS
#pythae #reproducibility #en #license-apache-2.0 #region-us 
